id stringlengths 23 40 | category stringclasses 17
values | sample_id stringlengths 5 14 | level stringclasses 3
values | input_type stringclasses 1
value | instruction stringlengths 1.54k 17.2k | GT w/o anno code stringlengths 264 15.4k | gt_wo_anno_chart imagewidth (px) 600 640 | GT code stringlengths 378 16.6k | gt_chart imagewidth (px) 400 640 |
|---|---|---|---|---|---|---|---|---|---|
Area_1_intent_code_image | Area | Area_1 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
visitors = [12, 120, 229, 990, 4104, 3250, 5720, 43152, 41251, 50458, 45012, 62548]
fig, ax = plt.subplots(figsize=(8, 6))
line, = ax.plot(months, visitors, marker='o', color=... | import matplotlib.pyplot as plt
import numpy as np
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
visitors = [12, 120, 229, 990, 4104, 3250, 5720, 43152, 41251, 50458, 45012, 62548]
fig, ax = plt.subplots(figsize=(8, 6))
line, = ax.plot(months, visitors, marker='o', color=... | ||
Area_2_intent_code_image | Area | Area_2 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
weeks = ['Week 40', 'Week 41', 'Week 42', 'Week 43', 'Week 44']
total_message = [5488135, 7151894, 6027634, 5448832, 4236548]
total_message_with_event = [645894, 437587, 891773, 963663, 383442]
fig, ax = plt.subplots(figsize=(10, 6))
ax.set_facecolor('#f5f5f5')
line1, ... | import matplotlib.pyplot as plt
import numpy as np
weeks = ['Week 40', 'Week 41', 'Week 42', 'Week 43', 'Week 44']
total_message = [5488135, 7151894, 6027634, 5448832, 4236548]
total_message_with_event = [645894, 437587, 891773, 963663, 383442]
fig, ax = plt.subplots(figsize=(10, 6))
ax.set_facecolor('#f5f5f5')
line1, ... | ||
Area_3_intent_code_image | Area | Area_3 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
quarters = ['', 'Quarter 1', 'Quarter 2', 'Quarter 3', 'Quarter 4']
region_a = [40, 50, 30, 43, 40]
region_b = [20, 35, 40, 22, 20]
fig, ax = plt.subplots(figsize=(10, 6))
ax.fill_between(quarters, region_a, color='#66C2A5', alpha=0.8, label='Region A')
ax.fill_between... | import matplotlib.pyplot as plt
import numpy as np
quarters = ['','Quarter 1', 'Quarter 2', 'Quarter 3', 'Quarter 4']
region_a = [40, 50, 30, 43, 40]
region_b = [20, 35, 40, 22, 20]
fig, ax = plt.subplots(figsize=(10, 6))
ax.fill_between(quarters, region_a, color='#66C2A5', alpha=0.8, label='Region A')
ax.fill_between(... | ||
Area_4_intent_code_image | Area | Area_4 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
x_plot = [0, 1, 2, 3, 4, 5, 6, 7]
y_plot = [0, 2, 4, 3, 6, 5, 2, 0]
fig, ax = plt.subplots(figsize=(6, 6))
ax.set_xlim(0, 7)
ax.set_ylim(0, 7)
ax.set_xticks(np.arange(0, 7, 1))
ax.set_yticks(np.arange(0, 7, 1))
ax.plot([0, 7], [0, 0], color='black', linewidth=2)
ax.plo... | import matplotlib.pyplot as plt
import numpy as np
x_plot =[0,1,2,3,4,5,6,7]
y_plot =[0,2,4,3,6,5,2,0]
x = x_plot[1:-1]
y = y_plot[1:-1]
fig, ax = plt.subplots(figsize=(6, 6))
ax.set_xlim(0, 7)
ax.set_ylim(0, 7)
ax.set_xticks(np.arange(0, 7, 1))
ax.set_yticks(np.arange(0, 7, 1))
ax.plot([0, 7], [0, 0], color='black', l... | ||
Area_5_intent_code_image | Area | Area_5 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from matplotlib.patches import Patch
dates = pd.date_range(start='1996-07-01', end='1998-04-01', freq='MS')
values = [
28000,
25000,
26500,
37500,
46000,
46000,
61258.07,
38500,
38500,
52500,
53500,
36... | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from matplotlib.patches import Patch
dates = pd.date_range(start='1996-07-01', end='1998-04-01', freq='MS')
values = [
28000,
25000,
26500,
37500,
46000,
46000,
61258.07,
38500,
38500,
52500,
53500,
36... | ||
Area_6_intent_code_image | Area | Area_6 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
quarters = ['', 'Quarter 1', 'Quarter 2', 'Quarter 3', 'Quarter 4']
region_a = [60, 85, 70, 65, 62]
region_b = [20, 35, 40, 22, 20]
fig, ax = plt.subplots(figsize=(10, 6))
ax.fill_between(quarters, region_a, color='#66C2A5', alpha=0.8, label='Region A')
ax.fill_between... | import matplotlib.pyplot as plt
import numpy as np
quarters = ['','Quarter 1', 'Quarter 2', 'Quarter 3', 'Quarter 4']
region_a = [60, 85, 70, 65, 62]
region_b = [20, 35, 40, 22, 20]
fig, ax = plt.subplots(figsize=(10, 6))
ax.fill_between(quarters, region_a, color='#66C2A5', alpha=0.8, label='Region A')
ax.fill_between(... | ||
Area_7_intent_code_image | Area | Area_7 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp1d
anchors_x = np.array([
0.00, 0.10, 0.20, 0.30, 0.40, 0.55, 0.70, 0.85, 0.95, 1.05,
1.15, 1.30, 1.45, 1.60, 1.75, 1.80, 1.90, 2.00, 2.10, 2.20,
2.35, 2.50, 2.65, 2.80, 2.95, 3.10, 3.25, 3.40, 3.55, 3.70,
3.85, 4.00,... | import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp1d
anchors_x = np.array([
0.00, 0.10, 0.20, 0.30, 0.40, 0.55, 0.70, 0.85, 0.95, 1.05,
1.15, 1.30, 1.45, 1.60, 1.75, 1.80, 1.90, 2.00, 2.10, 2.20,
2.35, 2.50, 2.65, 2.80, 2.95, 3.10, 3.25, 3.40, 3.55, 3.70,
3.85, 4.00,... | ||
Area_8_intent_code_image | Area | Area_8 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.lines import Line2D
np.random.seed(42)
components_ordered = [
('t17b17l0r0', 'red', 9.421, 0.048, 600),
('t22b21l1r1', 'orange', 11.538, 0.064, 600),
('t17b17l1r0', 'magenta', 9.890, 0.054, 550),
('t21b21l1r1', 'yellow', 11.567, 0.056, 6... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.lines import Line2D
np.random.seed(42)
components_ordered = [
('t17b17l0r0', 'red', 9.421, 0.048, 600),
('t22b21l1r1', 'orange', 11.538, 0.064, 600),
('t17b17l1r0', 'magenta', 9.890, 0.054, 550),
('t21b21l1r1', 'yellow', 11.567, 0.056, 6... | ||
Area_9_intent_code_image | Area | Area_9 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
countries = ['Canada', 'China', 'Russia', 'Australia', 'United States', 'France']
coal = [400, 800, 500, 400, 750, 350]
hydro = [350, 600, 400, 450, 700, 300]
nuclear = [200, 400, 300, 200, 500, 250]
gas = [150, 300, 250, 150, 400, 200]
oil = [100, 250, 200, 100, 300, ... | import matplotlib.pyplot as plt
import numpy as np
countries = ['Canada', 'China', 'Russia', 'Australia', 'United States', 'France']
coal = [400, 800, 500, 400, 750, 350]
hydro = [350, 600, 400, 450, 700, 300]
nuclear = [200, 400, 300, 200, 500, 250]
gas = [150, 300, 250, 150, 400, 200]
oil = [100, 250, 200, 100, 300, ... | ||
Area_10_intent_code_image | Area | Area_10 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
import pandas as pd
import platform
data = {
'Date': [
datetime(2012, 3, 20),
datetime(2012, 3, 22),
datetime(2012, 3, 23),
datetime(2012, 3, 24),
datetime(2012, 3, 25),
da... | import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
import pandas as pd
data = {
'Date': [
datetime(2012, 3, 20),
datetime(2012, 3, 22),
datetime(2012, 3, 23),
datetime(2012, 3, 24),
datetime(2012, 3, 25),
datetime(2012, 3, ... | ||
Area_11_intent_code_image | Area | Area_11 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as ticker
years = np.array([1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013])
y_upper = np.array([
2360, 2390, 2450, 2400, 2470, 2520, 2540, 2080, 2040, 1990,
... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as ticker
years = np.array([1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,
2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013])
y_upper = np.array([
2360, 2390, 2450, 2400, 2470, 2520, 2540, 2080, 2040, 1990,
... | ||
Area_12_intent_code_image | Area | Area_12 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.dates as mdates
from scipy.interpolate import interp1d
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans']
key_points = [
('1999-01-01', 200),
('2000-03-01', 480),
('200... | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.dates as mdates
from scipy.interpolate import interp1d
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans']
key_points = [
('1999-01-01', 200),
('2000-03-01', 480),
('200... | ||
Area_13_intent_code_image | Area | Area_13 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.transforms as transforms
np.random.seed(42)
def generate_segment_data_from_keyframes(start_yr, end_yr, keyframes, num_points):
x = np.linspace(start_yr, end_yr, num_points)
kf_x = [kf[0] for kf in keyframes]
kf_y = [np.log10(kf[1]) for kf ... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.transforms as transforms
np.random.seed(42)
def generate_segment_data_from_keyframes(start_yr, end_yr, keyframes, num_points):
x = np.linspace(start_yr, end_yr, num_points)
kf_x = [kf[0] for kf in keyframes]
kf_y = [np.log10(kf[1]) for kf ... | ||
Area_14_intent_code_image | Area | Area_14 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 6))
years = np.array([2014, 2015, 2016, 2017, 2018])
subsidized_data = np.array([4.5, 7.5, 8.2, 8.0, 8.3])
unsubsidized_data = np.array([3.7, 6.1, 6.3, 5.0, 3.8])
y_mid = np.array([
2235, 2250, 2310, 2270, 2360, 2410, 2435, 1975,... | import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 6))
years = np.array([2014, 2015, 2016, 2017, 2018])
subsidized_data = np.array([4.5, 7.5, 8.2, 8.0, 8.3])
unsubsidized_data = np.array([3.7, 6.1, 6.3, 5.0, 3.8])
ax.plot(years, subsidized_data, color='blue', marker='o', linewidth=2,... | ||
Area_15_intent_code_image | Area | Area_15 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans']
fig, ax = plt.subplots(figsize=(8, 6))
x_shift = 0.15
x_base = [
2007.00, 2007.25, 2007.50, 2007.75,
2008.00, 2008.25, 2008.35, 2008.50, 2008.75,
2009.00, ... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans']
fig, ax = plt.subplots(figsize=(8, 6))
x_shift = 0.15
x_base = [
2007.00, 2007.25, 2007.50, 2007.75,
2008.00, 200... | ||
Area_16_intent_code_image | Area | Area_16 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
years = np.array([2009, 2010, 2011, 2012, 2013, 2014, 2015])
seizures_kg = np.array([512, 1453, 1138, 513, 375, 128, 357])
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(years, seizures_kg, color='red', linewidth=5, marker='o', markersize=8,
markerfacecolor='w... | import matplotlib.pyplot as plt
import numpy as np
years = np.array([2009, 2010, 2011, 2012, 2013, 2014, 2015])
seizures_kg = np.array([512, 1453, 1138, 513, 375, 128, 357])
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(years, seizures_kg, color='red', linewidth=5, marker='o', markersize=8, markerfacecolor='white', m... | ||
Area_17_intent_code_image | Area | Area_17 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
years = np.array([1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
2009, 2010, 2011, 2012, 2013, 2014])
values = np.array([35, 58, 58, 30, -5, -50, -58, -62, -64, -65,
-85, -125, -155, -185, -210, -241])
fig, ax = plt.sub... | import matplotlib.pyplot as plt
import numpy as np
years = np.array([1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
2009, 2010, 2011, 2012, 2013, 2014])
values = np.array([35, 58, 58, 30, -5, -50, -58, -62, -64, -65,
-85, -125, -155, -185, -210, -241])
fig, ax = plt.sub... | ||
Area_18_intent_code_image | Area | Area_18 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
years = np.arange(1980, 2022)
n_years = len(years)
cpi_values = np.linspace(0, 236, n_years)
tuition_values = (np.linspace(0, 1, n_years)**1.3) * 1200
tuition_values[0] = 0
fig, ax = plt.subplots(figsize=(10, 10))
bg_color = '#E6E3DD'
fig.patch.set_facecolor(bg_color)
... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as patches
years = np.arange(1980, 2022)
n_years = len(years)
cpi_values = np.linspace(0, 236, n_years)
tuition_values = (np.linspace(0, 1, n_years)**1.3) * 1200
tuition_values[0] = 0
fig, ax = plt.subplots(figsize=(10, 10))
bg_color = '#E6E3D... | ||
Area_19_intent_code_image | Area | Area_19 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
from datetime import datetime
dates_str = [
'2014-01-01', '2014-06-01', '2014-11-01', '2015-01-01', '2015-06-01', '2015-12-01',
'2016-01-01', '2016-06-01', '2016-12-01', '2017-01-01', '2017-06-01', '2017-07-01',
'2017-12-01... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
from datetime import datetime
dates_str = [
'2014-01-01', '2014-06-01',
'2014-11-01',
'2015-01-01', '2015-06-01', '2015-12-01',
'2016-01-01', '2016-06-01', '2016-12-01',
'2017-01-01', '2017-06-01',
'2017-07-01',... | ||
Area_20_intent_code_image | Area | Area_20 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
import numpy as np
fig, ax = plt.subplots(figsize=(10, 6))
plt.title('Global coronavirus cases still rising fast', fontsize=20, fontweight='bold', pad=20)
dates = [
datetime(2019, 12, 31), datetime(2020, 2, 1), datetime(... | import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
import numpy as np
fig, ax = plt.subplots(figsize=(10, 6))
plt.title('Global coronavirus cases still rising fast', fontsize=20, fontweight='bold', pad=20)
dates = [
datetime(2019, 12, 31), datetime(2020, 2, 1), datetime(... | ||
Area_21_intent_code_image | Area | Area_21 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
from matplotlib.offsetbox import TextArea, HPacker, AnnotationBbox
import pandas as pd
import numpy as np
from datetime import datetime
start_date = datetime(2020, 1, 1)
end_date = datetime(2020, 6, 15)
dates = pd.date... | import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.offsetbox import TextArea, HPacker, VPacker, AnnotationBbox
import pandas as pd
import numpy as np
from datetime import datetime
start_date = datetime(2020, 1, 1)
end_date = datetime(2020, 6, 15)
dates = pd.date_range(start=start_date, en... | ||
Area_22_intent_code_image | Area | Area_22 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import make_interp_spline
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
days = np.linspace(0, 365, 365)
months = [0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334]
month_labels = ['1-Jan', '1-Feb', '1-Mar', '1-Apr'... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Rectangle
from scipy.interpolate import make_interp_spline
days = np.linspace(0, 365, 365)
months = [0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334]
month_labels = ['1-Jan', '1-Feb', '1-Mar', '1-Apr', '1-May', '1-Jun',
... | ||
Area_23_intent_code_image | Area | Area_23 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import PchipInterpolator
offences = np.array([1, 2, 5, 10])
costs = np.array([543, 888, 4128, 14928])
x_pts = np.array([0, 1, 2, 5, 10, 11])
y_pts = np.array([100, 543, 888, 4128, 14928, 18500])
pchip = PchipInterpolator(x_pts, y_pts)
fig, ax = p... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Rectangle, Polygon
from matplotlib.colors import LinearSegmentedColormap
from scipy.interpolate import make_interp_spline
offences = np.array([1, 2, 5, 10])
costs = np.array([543, 888, 4128, 14928])
fig, ax = plt.subplots(figsize=(12, 10)... | ||
Area_24_intent_code_image | Area | Area_24 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
jan_data = [
2.1, 2.3, 2.2, 3.5, 3.1, 2.8, 2.9, 3.2, 3.8, 3.5, 3.2, 3.4, 3.9, 4.1, 3.8,
3.6, 3.5, 3.4, 3.8, 4.2, 4.0, 3.9, 4.1, 4.5, 4.3, 4.8, 5.0, 4.9, 5.1, 5.2, 5.3
]
feb_data = [
5.5, 5.2, 5.8, 6.1, 6.0, 6.5, 7.0, 6.8, 6.5, 7.2, 7.5, 8.0, 7.8, 7.2, 6.5,
... | import matplotlib.pyplot as plt
import numpy as np
jan_data = [
2.1, 2.3, 2.2, 3.5, 3.1, 2.8, 2.9, 3.2, 3.8, 3.5, 3.2, 3.4, 3.9, 4.1, 3.8,
3.6, 3.5, 3.4, 3.8, 4.2, 4.0, 3.9, 4.1, 4.5, 4.3, 4.8, 5.0, 4.9, 5.1, 5.2, 5.3
]
feb_data = [
5.5, 5.2, 5.8, 6.1, 6.0, 6.5, 7.0, 6.8, 6.5, 7.2, 7.5, 8.0, 7.8, 7.2, 6.5,
... | ||
Area_25_intent_code_image | Area | Area_25 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
x_1800 = [0, 0.02, 0.20, 0.23, 0.28, 0.35, 0.38, 0.68, 0.72, 0.85, 0.90, 0.94, 0.98, 1.0]
y_1800 = [25, 26, 26, 29, 30, 31, 32, 32, 33, 35, 36, 38, 40, 40]
x_1950 = [0, 0.05, 0.15, 0.18, 0.38, 0.42, 0.48, 0.52, 0.58, 0.62, 0.70, 0.75, 0.82, 0.92, 0.96, 1.0]
y_1950 = [3... | import matplotlib.pyplot as plt
import numpy as np
x_1800 = [0, 0.02, 0.20, 0.23, 0.28, 0.35, 0.38, 0.68, 0.72, 0.85, 0.90, 0.94, 0.98, 1.0]
y_1800 = [25, 26, 26, 29, 30, 31, 32, 32, 33, 35, 36, 38, 40, 40]
x_1950 = [0, 0.05, 0.15, 0.18, 0.38, 0.42, 0.48, 0.52, 0.58, 0.62, 0.70, 0.75, 0.82, 0.92, 0.96, 1.0]
y_1950 = [3... | ||
Area_26_intent_code_image | Area | Area_26 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import make_interp_spline
from matplotlib.patches import Polygon
hist_years = np.array([1875, 1900, 1925, 1940, 1950, 1960, 1970, 1973, 1979, 1985, 1990, 2000, 2004])
hist_vals = np.array([0.5, 1.5, 3.0, 5.0, 7.0, 12.0, 21.0, 23.0, 24.0, 20.0, 23... | import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import make_interp_spline
from matplotlib.patches import Polygon
from matplotlib.colors import LinearSegmentedColormap
hist_years = np.array([1875, 1900, 1925, 1940, 1950, 1960, 1970, 1973, 1979, 1985, 1990, 2000, 2004])
hist_vals = np.array([0.5... | ||
Area_27_intent_code_image | Area | Area_27 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as patches
years = np.array([2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019])
base_deficit = np.array([0.02, 0.06, 0.18, 0.08, 0.01, 0.02, 0.01, 0.06, 0.04, 0.01, 0.05])
downturn = np.array([0.40, 0.46, 0.40, 0.35, 0.25, 0.22... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as patches
years = np.array([2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019])
base_deficit = np.array([0.02, 0.06, 0.18, 0.08, 0.01, 0.02, 0.01, 0.06, 0.04, 0.01, 0.05])
downturn = np.array([0.40, 0.46, 0.40, 0.35, 0.25, 0.22... | ||
Area_28_intent_code_image | Area | Area_28 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
data = [
(-2.5, 19, "FINANCIALS"),
(-5.0, -3, "UTILITIES"),
(6.0, 42, "CONSUMER\nCYCLICALS"),
(6.0, 12, "HEALTH CARE"),
(9.0, 19, "CAPITAL GOODS"),
(9.0, 1, "CONSUMER\nSTAPLES"),
(14.0, 3, "TECHNOLOGY"),
(15.0, 55, "BASIC MATERIALS"),
... | import matplotlib.pyplot as plt
import numpy as np
data = [
(-2.5, 19, "FINANCIALS"),
(-5.0, -3, "UTILITIES"),
(6.0, 42, "CONSUMER\nCYCLICALS"),
(6.0, 12, "HEALTH CARE"),
(9.0, 19, "CAPITAL GOODS"),
(9.0, 1, "CONSUMER\nSTAPLES"),
(14.0, 3, "TECHNOLOGY"),
(15.0, 55, "BASIC MATERIALS"),
... | ||
Area_29_intent_code_image | Area | Area_29 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
months_data = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEPT']
x = np.arange(len(months_data))
unemployed_width = np.array([5.8, 6.0, 7.5, 23.0, 21.5, 18.5, 17.0, 14.5, 12.5])
not_in_labor_width = np.array([6.2, 6.5, 7.0, 9.0, 8.5, 8.0, 7.5, 7.0, 6.5])
... | import matplotlib.pyplot as plt
import numpy as np
months_data = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEPT']
x = np.arange(len(months_data))
unemployed_width = np.array([5.8, 6.0, 7.5, 23.0, 21.5, 18.5, 17.0, 14.5, 12.5])
not_in_labor_width = np.array([6.2, 6.5, 7.0, 9.0, 8.5, 8.0, 7.5, 7.0, 6.5])
... | ||
Area_30_intent_code_image | Area | Area_30 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import make_interp_spline
years_raw = np.array([1900, 1910, 1915, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2012])
northeast_raw = np.array([0.8, 0.9, 1.0, 1.1, 1.1, 1.1, 1.1, 1.2, 1.3, 1.5, 1.8, 2.0, 2.2])
midwest_raw = np.array([3... | import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import make_interp_spline
years_raw = np.array([1900, 1910, 1915, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2012])
northeast_raw = np.array([0.8, 0.9, 1.0, 1.2, 1.2, 1.2, 1.2, 1.2, 1.5, 1.8, 2.0, 2.2, 2.5])
midwest_raw = np.array([3... | ||
Area_31_intent_code_image | Area | Area_31 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
x_main = np.linspace(0, 100, 150)
y_main = np.zeros_like(x_main)
def generate_jagged_curve(x, center, width, height, seed=42):
np.random.seed(seed)
y = height * np.exp(-((x - center)**2) / (2 * width**2))
noise = np.random.normal(0, 0.03 * height, len(x))
... | import matplotlib.pyplot as plt
import numpy as np
x_main = np.linspace(0, 100, 150)
y_main = np.zeros_like(x_main)
def generate_jagged_curve(x, center, width, height, seed=42):
np.random.seed(seed)
y = height * np.exp(-((x - center)**2) / (2 * width**2))
noise = np.random.normal(0, 0.03 * height, len(x))
... | ||
Area_32_intent_code_image | Area | Area_32 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
x_labels = ['128', '256', '512', '1K', '2K', '4K', '8K', '16K']
x_vals = [128, 256, 512, 1024, 2048, 4096, 8192, 16384]
x_pubmed = [128, 200, 300, 400, 600, 800, 1024, 2048, 16384]
y_pubmed = [0, 2, 20, 70, 98, 100, 100, 100, 100]
x_meeting = [128, 256, 512, 1024, 2048... | import matplotlib.pyplot as plt
import numpy as np
x_labels = ['128', '256', '512', '1K', '2K', '4K', '8K', '16K']
x_vals = [128, 256, 512, 1024, 2048, 4096, 8192, 16384]
x_pubmed = [128, 200, 300, 400, 600, 800, 1024, 2048, 16384]
y_pubmed = [0, 2, 20, 70, 98, 100, 100, 100, 100]
x_meeting = [128, 256, 512, 1024, 2048... | ||
Area_33_intent_code_image | Area | Area_33 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
plt.figure(figsize=(8, 8))
ax = plt.gca()
dwar_values = np.array([
-1.4, -1.3, -1.2, -1.1, -1.0, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1,
0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4
])
frequencies = np.array([
1, 0... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Polygon
plt.figure(figsize=(8, 8))
ax = plt.gca()
dwar_values = np.array([
-1.4, -1.3, -1.2, -1.1, -1.0, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1,
0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1... | ||
Bar_1_intent_code_image | Bar | Bar_1 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
models = ["GPT-2", "Llama 2 7B", "Llama 2 70B", "Mixtral 8x7B", "GPT-3.5", "GPT-4"]
simple = [0, 6, 8, 12, 12, 56]
complex = [0, 16, 4, 18, 10, 4]
code = [0, 12, 20, 26, 20, 22]
simple_trend = [0, 6, 8, 12, 12, 56]
labels = ["Simple", "Complex", "Code... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
models = ["GPT-2", "Llama 2 7B", "Llama 2 70B", "Mixtral 8x7B", "GPT-3.5", "GPT-4"]
simple = [0, 6, 8, 12, 12, 56]
complex = [0, 16, 4, 18, 10, 4]
code = [0, 12, 20, 26, 20, 22]
simple_trend = [0, 6, 8, 12, 12, 56]
labels = ["Simple", "Complex", "Code... | ||
Bar_2_intent_code_image | Bar | Bar_2 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["Female Player", "Male Player", "LLM Player", "Person Player"]
models = [
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-instruct",
"gpt-4",
"llama-2-13b",
"llama-2-70b",
]
values = np.random.rand(4, 5) * 5 + 3
colors = ["misty... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["Female Player", "Male Player", "LLM Player", "Person Player"]
models = [
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-instruct",
"gpt-4",
"llama-2-13b",
"llama-2-70b",
]
values = np.random.rand(4, 5) * 5 + 3
colors = ["misty... | ||
Bar_3_intent_code_image | Bar | Bar_3 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["Civil Case", "Criminal Case", "Corporate Case", "Family Case"]
models = ["Firm A", "Firm B", "Firm C", "Firm D", "Firm E"]
values = np.random.rand(4, 5) * 5 + 5
referlines = [5, 6, 8, 9, 10]
ylabel = "Average Win Rate (%)"
xlabel = 'Cas... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["Civil Case", "Criminal Case", "Corporate Case", "Family Case"]
models = ["Firm A", "Firm B", "Firm C", "Firm D", "Firm E"]
values = np.random.rand(4, 5) * 5 + 5
referlines = [5, 6, 8, 9, 10]
ylabel = "Average Win Rate (%)"
xlabel = 'Cas... | ||
Bar_4_intent_code_image | Bar | Bar_4 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
public_sector = [
300, 500, 800, 1200, 1500, 2000, 2500, 3000, 3500, 3800, 4000,
]
private_sector = [
1000, 1300, 1600, 1800, 2000, 2400, 2800, 3200, 3500, 3700, 3900,
]
bins = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
labels = [... | import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)
public_sector = [
300,
500,
800,
1200,
1500,
2000,
2500,
3000,
3500,
3800,
4000,
]
private_sector = [
1000,
1300,
1600,
1800,
2000,
2400,
2800,
3200,
3500,
3700,
... | ||
Bar_5_intent_code_image | Bar | Bar_5 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)
category1 = [
100,
200,
300,
400,
500,
500,
600,
800,
1000,
2000,
10000,
]
category2 = [
100,
200,
300,
400,
500,
800,
1000,
2000,
3000,
4000,
7000,
]
bins = [0.4... | import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)
category1 = [
100,
200,
300,
400,
500,
500,
600,
800,
1000,
2000,
10000,
]
category2 = [
100,
200,
300,
400,
500,
800,
1000,
2000,
3000,
4000,
7000,
]
bins = [0.4... | ||
Bar_6_intent_code_image | Bar | Bar_6 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
civil_cases = [500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500]
criminal_cases = [300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300]
years = range(2011, 2022)
labels = ["Civil Cases", "Criminal Cases"]
xlabel = "Years"
xlim = [2010... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
civil_cases = [500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500]
criminal_cases = [300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300]
years = range(2011, 2022)
labels = ["Civil Cases", "Criminal Cases"]
xlabel = "Years"
xlim = [2010... | ||
Bar_7_intent_code_image | Bar | Bar_7 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
public_school_scores = [
150,
240,
320,
480,
540,
600,
750,
820,
950,
1750,
8800,
]
private_school_scores = [
120,
210,
310,
370,
510,
760,
950,
1800,
2600,
3300,
... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
public_school_scores = [
150,
240,
320,
480,
540,
600,
750,
820,
950,
1750,
8800,
]
private_school_scores = [
120,
210,
310,
370,
510,
760,
950,
1800,
2600,
3300,
... | ||
Bar_8_intent_code_image | Bar | Bar_8 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(42)
moisture_levels = [0.0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.4, 0.45, 0.5]
wheat_yield = [
600,
750,
900,
1100,
1300,
1200,
1000,
800,
600,
500,
400,
]
corn_yield = [
400,
620,
850,
1... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(42)
moisture_levels = [0.0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.4, 0.45, 0.5]
wheat_yield = [
600,
750,
900,
1100,
1300,
1200,
1000,
800,
600,
500,
400,
]
corn_yield = [
400,
620,
850,
1... | ||
Bar_9_intent_code_image | Bar | Bar_9 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
category1 = [
1500, 2300, 2700, 3200, 2800, 2500, 2200, 1900, 1600, 1300, 1100
]
category2 = [
800, 1100, 1400, 1600, 1500, 1400, 1100, 900, 700, 600, 400
]
bins = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]
labels = ["Known A... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
category1 = [
1500, 2300, 2700, 3200, 2800, 2500, 2200, 1900, 1600, 1300, 1100
]
category2 = [
800, 1100, 1400, 1600, 1500, 1400, 1100, 900, 700, 600, 400
]
bins = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]
labels = ["Known A... | ||
Bar_10_intent_code_image | Bar | Bar_10 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
average_speed = [60, 45, 80, 20]
fuel_consumption = [8, 25, 5, 0]
x = np.arange(len(average_speed))
transport_modes = ["Car", "Bus", "Train", "Bicycle"]
labels = ["Average Speed (km/h)", "Fuel Consumption (L/100km)"]
title = "Transportation Analysis"
... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
average_speed = [60, 45, 80, 20]
fuel_consumption = [8, 25, 5, 0]
x = np.arange(len(average_speed))
transport_modes = ["Car", "Bus", "Train", "Bicycle"]
labels = ["Average Speed (km/h)", "Fuel Consumption (L/100km)"]
title = "Transportation Analysis"
... | ||
Bar_11_intent_code_image | Bar | Bar_11 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)
category1 = [
10000,
2000,
1000,
800,
600,
500,
500,
400,
300,
200,
100,
]
category2 = [
7000,
4000,
3000,
2000,
1000,
800,
500,
400,
300,
200,
100,
]
bins = [0.0... | import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)
category1 = [
10000,
2000,
1000,
800,
600,
500,
500,
400,
300,
200,
100,
]
category2 = [
7000,
4000,
3000,
2000,
1000,
800,
500,
400,
300,
200,
100,
]
bins = [0.0... | ||
Bar_12_intent_code_image | Bar | Bar_12 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
countries = [
"Canada", "Russia", "Brazil", "Mexico", "Australia", "India", "China", "USA",
"UK", "Germany", "France", "Japan", "South Korea", "South Africa", "Argentina",
"Italy", "Spain", "Turkey", "Saudi Arabia", "Indonesia", "Netherlands",
"Sweden",... | import matplotlib.pyplot as plt
import numpy as np
countries = [
"Canada",
"Russia",
"Brazil",
"Mexico",
"Australia",
"India",
"China",
"USA",
"UK",
"Germany",
"France",
"Japan",
"South Korea",
"South Africa",
"Argentina",
"Italy",
"Spain",
"Turkey... | ||
Bar_13_intent_code_image | Bar | Bar_13 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
tasks = [
"Analytic Philosophy", "Ethics", "Metaphysics", "Epistemology", "Logic",
"Aesthetics", "Political Philosophy", "Philosophy of Mind", "Philosophy of Science",
"Existentialism", "Critical Theory", "Hermeneutics", "Nihilism", "Idealism",
"Pragmat... | import matplotlib.pyplot as plt
import numpy as np
tasks = [
"Analytic Philosophy",
"Ethics",
"Metaphysics",
"Epistemology",
"Logic",
"Aesthetics",
"Political Philosophy",
"Philosophy of Mind",
"Philosophy of Science",
"Existentialism",
"Critical Theory",
"Hermeneutics",
... | ||
Bar_14_intent_code_image | Bar | Bar_14 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
subjects = ["Math", "Science", "English", "History"]
easy = [88, 85, 90, 78]
medium = [75, 70, 80, 65]
hard = [65, 60, 70, 55]
very_hard = [55, 50, 60, 45]
labels = ["Easy", "Medium", "Hard", "Very Hard"]
xlabel = "Subjects"
ylabel = "Performance Scor... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
subjects = ["Math", "Science", "English", "History"]
easy = [88, 85, 90, 78]
medium = [75, 70, 80, 65]
hard = [65, 60, 70, 55]
very_hard = [55, 50, 60, 45]
labels = ["Easy", "Medium", "Hard", "Very Hard"]
xlabel = "Subjects"
ylabel = "Performance Scor... | ||
Bar_15_intent_code_image | Bar | Bar_15 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
months = ["January", "February", "March", "April"]
new_york = [3.1, 2.9, 3.9, 4.1]
los_angeles = [3.4, 3.7, 2.9, 0.8]
chicago = [1.8, 1.5, 2.2, 3.7]
houston = [3.5, 2.9, 3.3, 4.5]
labels = ["New York", "Los Angeles", "Chicago", "Houston"]
xlabel = "Mo... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
months = ["January", "February", "March", "April"]
new_york = [3.1, 2.8, 3.9, 4.1]
los_angeles = [3.4, 3.7, 2.9, 0.8]
chicago = [1.8, 1.5, 2.2, 3.7]
houston = [3.5, 2.9, 3.3, 4.5]
labels = ["New York", "Los Angeles", "Chicago", "Houston"]
xlabel = "Mo... | ||
Bar_16_intent_code_image | Bar | Bar_16 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
categories = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
synthetic_data = [-3, -5, -2, -6, -4, -3, -2]
human_data = [5, 7, 3, 8, 6, 5, 4]
labels = ["Synthetic Temperature Data", "Recorded Temperature Data"]
xlabel = "Temperature Varia... | import matplotlib.pyplot as plt
import numpy as np
categories = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
synthetic_data = [-3, -5, -2, -6, -4, -3, -2]
human_data = [5, 7, 3, 8, 6, 5, 4]
labels = ["Synthetic Temperature Data", "Recorded Temperature Data"]
xlabel = "Temperature Varia... | ||
Bar_17_intent_code_image | Bar | Bar_17 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
np.random.seed(42)
categories = [
"Homework Completion",
"Quiz Scores",
"Class Participation",
"Project Submission",
"Attendance",
"Exam Scores",
"Group Activities",
]
differences = np.random.randint(-80, ... | import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
np.random.seed(42)
categories = [
"Homework Completion",
"Quiz Scores",
"Class Participation",
"Project Submission",
"Attendance",
"Exam Scores",
"Group Activities",
]
differences = np.random.randint(-80, ... | ||
Bar_18_intent_code_image | Bar | Bar_18 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
np.random.seed(0)
categories = [
"Ethics",
"Metaphysics",
"Epistemology",
"Logic",
"Aesthetics",
"Philosophy of Mind",
"Political Philosophy",
]
differences = np.random.randint(-100, 101, len(categories))
... | import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
np.random.seed(0)
categories = [
"Ethics",
"Metaphysics",
"Epistemology",
"Logic",
"Aesthetics",
"Philosophy of Mind",
"Political Philosophy",
]
differences = np.random.randint(-100, 101, len(categories))
... | ||
Bar_19_intent_code_image | Bar | Bar_19 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
categories = [
"running",
"cycling",
"swimming",
"weightlifting",
"yoga",
"basketball",
"soccer",
]
synthetic_data = [-10, -15, -20, -5, -8, -12, -7]
human_data = [30, 45, 25, 20, 15, 30, 40]
chart_title =... | import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
categories = [
"running",
"cycling",
"swimming",
"weightlifting",
"yoga",
"basketball",
"soccer",
]
synthetic_data = [-10, -15, -20, -5, -8, -12, -7]
human_data = [30, 45, 25, 20, 15, 30, 40]
chart_title =... | ||
Bar_20_intent_code_image | Bar | Bar_20 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
categories = [
"research",
"development",
"testing",
"deployment",
"maintenance",
"documentation",
"customer support",
]
synthetic_data = [
-10,
-30,
-20,
-15,
-25,
-18,
-12,
]
... | import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
categories = [
"research",
"development",
"testing",
"deployment",
"maintenance",
"documentation",
"customer support",
]
synthetic_data = [
-10,
-30,
-20,
-15,
-25,
-18,
-12,
]
... | ||
Bar_21_intent_code_image | Bar | Bar_21 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
categories = [
"Beach Resort",
"National Park",
"Museum",
"Historic Site",
"Theme Park",
"Zoo",
"Aquarium",
]
layer_data = {
f"Visitors from {country}": np.random.randint(1000, 5000, size=len(categories))
for countr... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
categories = [
"Beach Resort",
"National Park",
"Museum",
"Historic Site",
"Theme Park",
"Zoo",
"Aquarium",
]
layer_data = {
f"Visitors from {country}": np.random.randint(1000, 5000, size=len(categories))
for countr... | ||
Bar_22_intent_code_image | Bar | Bar_22 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["Treatment A", "Treatment B", "Placebo"]
values1 = [25, 30, 45]
values2 = [35, 29, 39]
values3 = [28, 34, 31]
values4 = [40, 33, 36]
values5 = [32, 28, 30]
values1minus = [-15, -20, -10]
values2minus = [-10, -15, -12]
values3minus = [-18... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["Treatment A", "Treatment B", "Placebo"]
values1 = [25, 30, 45]
values2 = [35, 29, 39]
values3 = [28, 34, 31]
values4 = [40, 33, 36]
values5 = [32, 28, 30]
values1minus = [-15, -20, -10]
values2minus = [-10, -15, -12]
values3minus = [-18... | ||
Bar_23_intent_code_image | Bar | Bar_23 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["Teacher A", "Teacher B", "Online Course", "Group Discussion"]
models = [
"Lecture",
"Flipped Classroom",
"Online Learning",
"Experiment-Based",
"Project-Based",
]
values = np.random.rand(4, 5) * 5 + 3
ylabel = "Avera... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["Teacher A", "Teacher B", "Online Course", "Group Discussion"]
models = [
"Lecture",
"Flipped Classroom",
"Online Learning",
"Experiment-Based",
"Project-Based",
]
values = np.random.rand(4, 5) * 5 + 3
referlines = [3... | ||
Bar_24_intent_code_image | Bar | Bar_24 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
resolutions = ["224", "128", "64", "32"]
imagenet_1k = [82, 60, 40, 35]
imagenet_f = [59, 50, 30, 10]
pac_fno_imagenet_1k = [0, 2, 10, 30]
pac_fno_imagenet_f = [5, 10, 20, 20]
labels = ["ImageNet-1k", "PAC-FNO", "ImageNet (F)", "PAC-FNO"]
xlabel = "Re... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
resolutions = ["224", "128", "64", "32"]
imagenet_1k = [82, 60, 40, 35]
imagenet_f = [59, 50, 30, 10]
pac_fno_imagenet_1k = [0, 2, 10, 30]
pac_fno_imagenet_f = [5, 10, 20, 20]
labels = ["ImageNet-1k", "PAC-FNO", "ImageNet (F)", "PAC-FNO"]
xlabel = "Re... | ||
Bar_25_intent_code_image | Bar | Bar_25 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["λ=0.06", "λ=0.08", "λ=0.1"]
values1 = [39.4, 35.18, 34.06]
values2 = [32.84, 20.84, 30.84]
values3 = [19.66, 28.0, 24.27]
values4 = [26.82, 30, 34.06]
values5 = [22, 22, 22]
values1minus = [-17, -19, -16]
values2minus = [-9, -12, -14]
v... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["λ=0.06", "λ=0.08", "λ=0.1"]
values1 = [39.4, 35.18, 34.06]
values2 = [32.84, 20.84, 30.84]
values3 = [19.66, 28.0, 24.27]
values4 = [26.82, 30, 34.06]
values5 = [22, 22, 22]
values1minus = [-17, -19, -16]
values2minus = [-9, -12, -14]
v... | ||
Bar_26_intent_code_image | Bar | Bar_26 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
categories = [
"greeting",
"request",
"criticism",
"apology",
"persuasion",
"thanking",
"leave-taking",
]
synthetic_data = [
-8,
-31,
-24,
-7,
-10,
-10,
-10,
]
human_data = [16, 28, 11, 5, 15, 16, 9]
labels = ["synthetic data", "hum... | import matplotlib.pyplot as plt
categories = [
"greeting",
"request",
"criticism",
"apology",
"persuasion",
"thanking",
"leave-taking",
]
synthetic_data = [
-8,
-31,
-24,
-7,
-10,
-10,
-10,
]
human_data = [16, 28, 11, 5, 15, 16, 9]
labels = ["synthetic data", "hum... | ||
Bar_27_intent_code_image | Bar | Bar_27 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
models = ["GRU4Rec", "Caser", "SASRec", "BERT4Rec", "FMLP-Rec"]
yelp_values = [19.7, -15.9, -8.5, 4.8, -0.7]
ax_title = "Yelp"
xticks_values = range(-20, 21, 5)
xlabel = "▲%"
fig, ax = plt.subplots(figsize=(10, 8))
bars = ax.barh(models, yelp_values, color="white", edgecolor="black", hat... | import matplotlib.pyplot as plt
models = ["GRU4Rec", "Caser", "SASRec", "BERT4Rec", "FMLP-Rec"]
yelp_values = [19.7, -15.9, -8.5, 4.8, -0.7]
ax_title = "Yelp"
xticks_values = range(-20, 21, 5)
xlabel = "▲%"
fig, ax = plt.subplots(figsize=(10, 8))
bars = ax.barh(models, yelp_values, color="white", edgecolor="black", hat... | ||
Bar_28_intent_code_image | Bar | Bar_28 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)
categories = ["LLAMA-Default", "LLAMA-HAG", "Vicuna-Default", "Vicuna-HAG"]
num_scores = 4
score_range = (-3.5, -0.5)
scores_3 = np.random.uniform(score_range[0], score_range[1], num_scores).tolist()
scores_5 = np.random.uniform(score_range[0], score_... | import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
categories = ["LLAMA-Default", "LLAMA-HAG", "Vicuna-Default", "Vicuna-HAG"]
num_scores = 4
score_range = (-3.5, -0.5)
scores_3 = np.random.uniform(score_range[0], score_range[1], num_scores).tolist()
scores_5 = np.random.uniform(score_range[0], score_... | ||
Bar_29_intent_code_image | Bar | Bar_29 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["λ=0.06", "λ=0.08", "λ=0.1"]
values1 = [39.4, 35.18, 34.06]
values2 = [32.84, 20.84, 30.84]
values3 = [19.66, 28.0, 24.27]
values4 = [26.82, 30, 34.06]
values5 = [22, 22, 22]
values1minus = [-17, -19, -16]
values2minus = [-9, -12, -14]
v... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
categories = ["λ=0.06", "λ=0.08", "λ=0.1"]
values1 = [39.4, 35.18, 34.06]
values2 = [32.84, 20.84, 30.84]
values3 = [19.66, 28.0, 24.27]
values4 = [26.82, 30, 34.06]
values5 = [22, 22, 22]
values1minus = [-17, -19, -16]
values2minus = [-9, -12, -14]
v... | ||
Bar_30_intent_code_image | Bar | Bar_30 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
truthful_recall = [46.3, 57.9, 53.8, 19.4]
misleading_recall = [30.1, 34, 43.7, 20]
x = np.arange(len(truthful_recall))
labels = ["Truthful Recall", "Misleading Recall"]
title = "Truthful:Misleading = 2:0"
ylim1 = [-60, 60]
ylim2 = [-50, 50]
yticks1 =... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
truthful_recall = [46.3, 57.9, 53.8, 19.4]
misleading_recall = [30.1, 34, 43.7, 20]
x = np.arange(len(truthful_recall))
labels = ["Truthful Recall", "Misleading Recall"]
title = "Truthful:Misleading = 2:0"
ylim1 = [-60, 60]
ylim2 = [-50, 50]
yticks1 =... | ||
Bar_31_intent_code_image | Bar | Bar_31 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import numpy as np
import matplotlib.pyplot as plt
category_names = [
"Strongly disagree",
"Disagree",
"Neither agree nor disagree",
"Agree",
"Strongly agree",
]
results = {
"Question 1": [10, 15, 17, 32, 26],
"Question 2": [26, 22, 29, 10, 13],
"Question 3": [35, 37, 15, 12, 19],
"Q... | import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
category_names = [
"Strongly disagree",
"Disagree",
"Neither agree nor disagree",
"Agree",
"Strongly agree",
]
results = {
"Question 1": [10, 15, 17, 32, 26],
"Question 2": [26, 22, 29, 10, 13],
"Question 3": [35, 37, 1... | ||
Bar_32_intent_code_image | Bar | Bar_32 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
labels = [
"Model_A",
"Model_B",
"Model_C",
"Model_D",
"Model_E",
"Model_F",
"Model_G",
"Model_H",
]
baseline_scores = [63, 75, 47, 85, 53, 77, 68, 92]
improved_scores = [70, 80, 55, 90, 60, 83, 74, 95]
label_Baseline =... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
labels = [
"Model_A",
"Model_B",
"Model_C",
"Model_D",
"Model_E",
"Model_F",
"Model_G",
"Model_H",
]
baseline_scores = [63, 75, 47, 85, 53, 77, 68, 92]
improved_scores = [70, 80, 55, 90, 60, 83, 74, 95]
line_y_1 = 60
li... | ||
Bar_33_intent_code_image | Bar | Bar_33 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
labels = [
"Telephone", "Postal Mail", "Fax", "Email", "Social Media", "Instant Messaging", "Video Calling"
]
traditional_methods = [30, 45, 40, 0, 0, 0, 0]
digital_methods = [0, 0, 0, 25, 35, 50, 55]
ylabel_value = "Efficiency Metric"
title_value... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
labels = [
"Telephone", "Postal Mail", "Fax", "Email", "Social Media", "Instant Messaging", "Video Calling"
]
traditional_methods = [30, 45, 40, 0, 0, 0, 0]
digital_methods = [0, 0, 0, 25, 35, 50, 55]
line_y_1 = 30
line_y_2 = 40
line_x_1 = 2.5
lin... | ||
Bar_34_intent_code_image | Bar | Bar_34 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
labels = [
"Bus", "Train", "Car", "Bicycle", "Walk", "Motorcycle", "Scooter",
"Taxi", "Ferry", "Tram", "Plane", "Subway", "Helicopter", "Cable Car",
]
non_aggregation = [12.3, 8.5, 15.7, 0, 0, 10.2, 7.8, 14.3, 0, 0, 0, 0, 0, 0]
aggregation = [... | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
labels = [
"Bus",
"Train",
"Car",
"Bicycle",
"Walk",
"Motorcycle",
"Scooter",
"Taxi",
"Ferry",
"Tram",
"Plane",
"Subway",
"Helicopter",
"Cable Car",
]
non_aggregation = [12.3, 8.5, 15.7, 0, 0, 10... | ||
Bar_35_intent_code_image | Bar | Bar_35 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
population_segments = [
"Poorest 5%", "Next 5%", "Next 5%", "Next 5%", "Next 5%",
"Next 5%", "Next 5%", "Next 5%", "Next 5%", "Next 5%",
"Next 5%", "Next 5%", "Next 5%", "Next 5%", "Next 5%",
"Next 5%", "Next 5%", "Next 5%", "Next 5%", "Richest 5%"
]
emission_growth = [
... | import matplotlib.pyplot as plt
population_segments = [
"Poorest 5%", "Next 5%", "Next 5%", "Next 5%", "Next 5%",
"Next 5%", "Next 5%", "Next 5%", "Next 5%", "Next 5%",
"Next 5%", "Next 5%", "Next 5%", "Next 5%", "Next 5%",
"Next 5%", "Next 5%", "Next 5%", "Next 5%", "Richest 5%"
]
emission_gro... | ||
Bar_36_intent_code_image | Bar | Bar_36 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['PingFang HK']
plt.rcParams['axes.unicode_minus'] = False
months = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月']
values = [1100, 1300, 800, 600, 400, 500, 0, -300, -500, 1700, 1600, 700]
fig, ax = plt.subplots(figsize=(12, ... | import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['PingFang HK']
plt.rcParams['axes.unicode_minus'] = False
months = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月']
values = [1100, 1300, 800, 600, 400, 500, 0, -300, -500, 1700, 1600, 700]
fig, ax = plt.subplots(figsize=(12, ... | ||
Bar_37_intent_code_image | Bar | Bar_37 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 7))
years = ["2017", "2018", "2019", "2020", "2021", "2022"]
s1 = np.array([20, 21, 21, 22, 23, 24])
s2 = np.array([11, 11, 11, 12, 13, 14])
s3 = np.array([20, 22, 24, 25, 26, 27])
s4 = np.array([20, 21, 22, 23, 24, 25])
totals = s1 ... | import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 7))
years = ["2017", "2018", "2019", "2020", "2021", "2022"]
s1 = np.array([20, 21, 21, 22, 23, 24])
s2 = np.array([11, 11, 11, 12, 13, 14])
s3 = np.array([20, 22, 24, 25, 26, 27])
s4 = np.array([20, 21, 22, 23, 24, 25])
totals = s1 ... | ||
Bar_38_intent_code_image | Bar | Bar_38 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
years = ['2017', '2018', '2019']
blue_data = np.array([13, 28, 44])
dark_green_data = np.array([5, 4, 7])
light_green_data = np.array([4, 5, 4])
bottom_dark = blue_data
bottom_light = blue_data + dark_green_data
totals = blue_data + dark_green_data + light_green_data
c... | import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
years = ['2017', '2018', '2019']
blue_data = np.array([13, 28, 44])
dark_green_data = np.array([5, 4, 7])
light_green_data = np.array([4, 5, 4])
bottom_dark = blue_data
bottom_light = blue_data + dark_green_data
totals = blue_data +... | ||
Bar_39_intent_code_image | Bar | Bar_39 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
years = ['2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018*', '2019*', '2020*', '2021*', '2022*', '2023*', '2024*', '2025*']
data_values = [5, 6.5, 9, 12.5, 15.5, 18, 26, 33, 41, 64.2, 79, 97, 120, 147, 181]
fig, ax = plt.subplots(figsize=(12, 6))
bars = ax.bar(years, data_va... | import matplotlib.pyplot as plt
years = ['2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018*', '2019*', '2020*', '2021*', '2022*', '2023*', '2024*', '2025*']
data_values = [5, 6.5, 9, 12.5, 15.5, 18, 26, 33, 41, 64.2, 79, 97, 120, 147, 181]
fig, ax = plt.subplots(figsize=(12, 6))
bars = ax.bar(years, data_va... | ||
Bar_40_intent_code_image | Bar | Bar_40 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
fig, ax = plt.subplots(figsize=(10, 4), dpi=100)
fig.patch.set_facecolor('white')
ax.set_facecolor('white')
years = np.arange(1746, 1816)
values = [
23000, 29000, 43000, 30000, 48000, 33000, 33000, 51000, 26000, 38000,
37000, ... | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.patches as mpatches
import numpy as np
fig, ax = plt.subplots(figsize=(10, 4), dpi=100)
fig.patch.set_facecolor('white')
ax.set_facecolor('white')
years = np.arange(1746, 1816)
values = [
23000, 29000, 43000, 30000, 48000, 33000, 3... | ||
Bar_41_intent_code_image | Bar | Bar_41 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 6))
valuation_methods = [
"52 Week Trading Range",
"Current Analyst Forecast",
"Comparables Valuation",
"Precedent Transaction Valuation",
"DCF Valuation"
]
data = [
(22.68, 40.54),
(45.00, 48.00),
(31... | import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 6))
valuation_methods = [
"52 Week Trading Range",
"Current Analyst Forecast",
"Comparables Valuation",
"Precedent Transaction Valuation",
"DCF Valuation"
]
data = [
(22.68, 40.54),
(45.00, 48.00),
(31... | ||
Bar_42_intent_code_image | Bar | Bar_42 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
age_groups = ['16-24', '25-34', '35-44', '45-54', '55-64', '65-up']
women_data = [6047, 7124, 7653, 6915, 1000, 800]
men_data = [1000, 16683, 17916, 600, 12304, 7199]
age_groups = age_groups[::-1]
women_data = women_data[::-1]
men_data = men_data[::-1]
fig, ax = plt.su... | import matplotlib.pyplot as plt
import numpy as np
age_groups = ['16-24', '25-34', '35-44', '45-54', '55-64', '65-up']
women_data = [6047, 7124, 7653, 6915, 1000, 800]
men_data = [1000, 16683, 17916, 600, 12304, 7199]
age_groups = age_groups[::-1]
women_data = women_data[::-1]
men_data = men_data[::-1]
fig, ax = plt.su... | ||
Bar_43_intent_code_image | Bar | Bar_43 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
countries = ['Canada', 'China', 'Russia', 'Australia', 'United States', 'France']
categories = ['Coal', 'Hydro', 'Nuclear', 'Gas', 'Oil']
data = {
'Coal': np.array([400, 820, 500, 420, 780, 380]),
'Hydro': np.array([350, 630, 410, 360, 720, 300]),
'Nuc... | import matplotlib.pyplot as plt
import numpy as np
countries = ['Canada', 'China', 'Russia', 'Australia', 'United States', 'France']
categories = ['Coal', 'Hydro', 'Nuclear', 'Gas', 'Oil']
data = {
'Coal': np.array([400, 820, 500, 420, 780, 380]),
'Hydro': np.array([350, 630, 410, 360, 720, 300]),
'Nuc... | ||
Bar_44_intent_code_image | Bar | Bar_44 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
ticket_classes = ['First', 'Second', 'Third']
female_ages = [35, 29, 22]
male_ages = [41, 31, 27]
x = np.arange(len(ticket_classes))
width = 0.35
fig, ax = plt.subplots(figsize=(8, 6))
rects1 = ax.bar(x - width/2, female_ages, width, label='female', color="#9CDDAE")
re... | import matplotlib.pyplot as plt
import numpy as np
ticket_classes = ['First', 'Second', 'Third']
female_ages = [35, 29, 22]
male_ages = [41, 31, 27]
x = np.arange(len(ticket_classes))
width = 0.35
fig, ax = plt.subplots(figsize=(8, 6))
rects1 = ax.bar(x - width/2, female_ages, width, label='female', color="#9CDDAE")
re... | ||
Bar_45_intent_code_image | Bar | Bar_45 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
labels = ['G1', 'G2', 'G3', 'G4', 'G5']
men_means = [20, 35, 30, 35, 27]
men_std = [2, 3, 4, 1, 2]
women_means = [25, 32, 34, 20, 25]
women_std = [3, 5, 2, 3, 3]
x = np.arange(len(labels))
width = 0.35
fig, ax = plt.subplots(figsize=(8, 6))
rects1 = ax.bar(x - width/2,... | import matplotlib.pyplot as plt
import numpy as np
labels = ['G1', 'G2', 'G3', 'G4', 'G5']
men_means = [20, 35, 30, 35, 27]
men_std = [2, 3, 4, 1, 2]
women_means = [25, 32, 34, 20, 25]
women_std = [3, 5, 2, 3, 3]
x = np.arange(len(labels))
width = 0.35
fig, ax = plt.subplots(figsize=(8, 6))
rects1 = ax.bar(x - width/2,... | ||
Bar_46_intent_code_image | Bar | Bar_46 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
regions = ["Central", "East", "South", "West"]
segments = ["Consumer", "Corporate", "Home Office"]
values = {
"Central": [250000, 150000, 100000],
"East": [350000, 200000, 130000],
"South": [200000, 120000, 80000],
"West": [360000, 230000, 14000... | import matplotlib.pyplot as plt
import numpy as np
regions = ["Central", "East", "South", "West"]
segments = ["Consumer", "Corporate", "Home Office"]
values = {
"Central": [250000, 150000, 100000],
"East": [350000, 200000, 130000],
"South": [200000, 120000, 80000],
"West": [360000, 230000, 14000... | ||
Bar_47_intent_code_image | Bar | Bar_47 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
quarters = ['2020 Q1', '2020 Q2', '2020 Q3', '2020 Q4']
data = {
'2020 Q1': {'Lincoln': 52.8, 'Kent': 44.7, 'Mersey': 43.5, 'York': 38.8},
'2020 Q2': {'Kent': 45.0, 'Mersey': 41.0, 'Lincoln': 36.5, 'York': 34.1},
'2020 Q3': {'Kent': 51.2, 'Lincoln': 44.2, '... | import matplotlib.pyplot as plt
import numpy as np
quarters = ['2020 Q1', '2020 Q2', '2020 Q3', '2020 Q4']
data = {
'2020 Q1': {'Lincoln': 52.8, 'Kent': 44.7, 'Mersey': 43.5, 'York': 38.8},
'2020 Q2': {'Kent': 45.0, 'Mersey': 41.0, 'Lincoln': 36.5, 'York': 34.1},
'2020 Q3': {'Kent': 51.2, 'Lincoln': 44.2, '... | ||
Bar_48_intent_code_image | Bar | Bar_48 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
strategies = ['Strategy 1', 'Strategy 2', 'Strategy 3', 'Strategy 4', 'Strategy 5']
product_a = [100, 105, 120, 90, 118]
product_b = [101, 140, 80, 80, 99]
product_c = [140, 145, 160, 162, 138]
product_d = [70, 152, 145, 170, 20]
co... | import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
strategies = ['Strategy 1', 'Strategy 2', 'Strategy 3', 'Strategy 4', 'Strategy 5']
product_a = [100, 105, 120, 90, 118]
product_b = [101, 140, 80, 80, 99]
product_c = [140, 145, 160, 162, 138]
product_d = [70, 152, 145, 170, 20]
co... | ||
Bar_49_intent_code_image | Bar | Bar_49 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
categories = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [10, 20, 30, 40, 50, 60]
cost = [7, 15, 24, 33, 42, 51]
profit = [5, 5.5, 7, 7.5, 8.5, 9.5]
roi = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
y_pos = np.arange(len(categories))
bar_he... | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
categories = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [10, 20, 30, 40, 50, 60]
cost = [7, 15, 24, 33, 42, 51]
profit = [5, 5.5, 7, 7.5, 8.5, 9.5]
roi = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
mov_avg = []
mov_avg_categories = []
for ... | ||
Bar_50_intent_code_image | Bar | Bar_50 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
def darken_color(color, factor=0.8):
from matplotlib.colors import to_rgb, to_hex
r, g, b = to_rgb(color)
return to_hex((r * factor, g * factor, b * factor))
categories = ["Value Marriage\n+9.4%", "Lifetime Union\n+7.3%"... | import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
def darken_color(color, factor=0.8):
from matplotlib.colors import to_rgb, to_hex
r, g, b = to_rgb(color)
return to_hex((r * factor, g * factor, b * factor))
categories = ["Value Marriage\n+9.4%", "Lifetime Union\n+7.3%"... | ||
Bar_51_intent_code_image | Bar | Bar_51 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
data = [
(3.6, 9.3, 5.6),
(5.0, 7.0, 5.6),
(-0.1, 6.9, 3.1),
(2.9, 3.8, 3.1),
(4.1, 10.6, 6.5),
(5.8, 7.2, 6.5),
(1.7, 7.6, 4.7),
(3.9, 5.1, 4.7),
(3.7, 11.5, 6.8),
(5.2, 8.3, 6.8),
(2.3,... | import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
data = [
(3.6, 9.3, 5.6),
(5.0, 7.0, 5.6),
(-0.1, 6.9, 3.1),
(2.9, 3.8, 3.1),
(4.1, 10.6, 6.5),
(5.8, 7.2, 6.5),
(1.7, 7.6, 4.7),
(3.9, 5.1, 4.7),
(3.7, 11.5, 6.8),
(5.2, 8.3, 6.8),
(2.3, ... | ||
Bar_52_intent_code_image | Bar | Bar_52 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Patch
means_small = [0.20, 0.30, 0.13, 0.18]
means_medium = [0.07, 0.27, 0.12, 0.14]
means_large = [0.09, 0.13, 0.06, 0.14]
err_small = [0.02, 0.02, 0.02, 0.02]
err_medium = [0.02, 0.02, 0.02, 0.02]
err_large = [0.02, 0.02, 0.02, 0.02]
la... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Rectangle
means_small = [0.20, 0.30, 0.13, 0.18]
means_medium = [0.07, 0.27, 0.12, 0.14]
means_large = [0.09, 0.13, 0.06, 0.14]
err_small = [0.02, 0.02, 0.02, 0.02]
err_medium = [0.02, 0.02, 0.02, 0.02]
err_large = [0.02, 0.02, 0.02, 0.02... | ||
Bar_53_intent_code_image | Bar | Bar_53 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
tasks = ['Task 1', 'Task 2', 'Task 3', 'Task 4', 'Task 5', 'Task 6', 'Task 7']
data_segments = [
[2, 6, 17, 25, 50],
[1, 5, 6, 25, 63],
[3, 8, 10, 24, 55],
[3, 8, 15, 24, 50],
[5, 14, 18, 20, 43],
[1, 5, 11, 20, 63],
[3, 8, 14, 25, 50]
]
lab... | import matplotlib.pyplot as plt
import numpy as np
tasks = ['Task 1', 'Task 2', 'Task 3', 'Task 4', 'Task 5', 'Task 6', 'Task 7']
data_segments = [
[2, 6, 17, 25, 50],
[1, 5, 6, 25, 63],
[3, 8, 10, 24, 55],
[3, 8, 15, 24, 50],
[5, 14, 18, 20, 43],
[1, 5, 11, 20, 63],
[3, 8, 14, 25, 50]
]
lab... | ||
Bar_54_intent_code_image | Bar | Bar_54 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.family'] = 'serif'
categories = [
"Furniture",
"Clothing",
"Sporting Goods",
"Electronics & Appliances",
"Health Goods",
"Toys",
"Home Goods",
"Groceries"
]
data_1992 = [0.010, 0.043, 0.067, 0.075, 0.031, 0.048, 0.018,... | import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.family'] = 'serif'
categories = [
"Furniture",
"Clothing",
"Sporting Goods",
"Electronics & Appliances",
"Health Goods",
"Toys",
"Home Goods",
"Groceries"
]
data_1992 = [0.010, 0.043, 0.067, 0.075, 0.031, 0.048, 0.018,... | ||
Bar_55_intent_code_image | Bar | Bar_55 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.family'] = 'serif'
data = np.array([
[66, 62, 45, 79, 100],
[9, 5, 11, 27, 100],
[64, 62, 44, 76, 100],
[9, 5, 13, 27, 43],
[68, 60, 46, 66, 38],
[10, 5, 10, 26, 9],
[67, 60, 48, 64, 14],
[9, 6, 9, 26, 3],
... | import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.family'] = 'serif'
data = np.array([
[66, 62, 45, 79, 100],
[9, 5, 11, 27, 100],
[64, 62, 44, 76, 100],
[9, 5, 13, 27, 43],
[68, 60, 46, 66, 38],
[10, 5, 10, 26, 9],
[67, 60, 48, 64, 14],
[9, 6, 9, 26, 3],
... | ||
Bar_56_intent_code_image | Bar | Bar_56 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
categories = ['associative', 'ceaser', 'ceaser-s', 'newcache', 'phantom', 'scatter-cache', 'set-associative']
data = {
'PPP': [7e10, 1.8e8, 6e7, 8e9, 1.2e9, 5e7, 1.5e8],
'GEM': [1.5e9, 7e8, 2e8, 1.2e8, 4.5e9, 2e8, 7e8],
'SHM': [2e10, 1.8e10, 1.5e10, 1.5e10,... | import matplotlib.pyplot as plt
import numpy as np
categories = ['associative', 'ceaser', 'ceaser-s', 'newcache', 'phantom', 'scatter-cache', 'set-associative']
data = {
'PPP': [7e10, 1.8e8, 6e7, 8e9, 1.2e9, 5e7, 1.5e8],
'GEM': [1.5e9, 7e8, 2e8, 1.2e8, 4.5e9, 2e8, 7e8],
'SHM': [2e10, 1.8e10, 1.5e10, 1.5e10,... | ||
Bar_57_intent_code_image | Bar | Bar_57 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as patches
labels = ['Case 1', 'Case 2', 'Case 3', 'Case 4']
x_pos = np.arange(len(labels))
semcom = np.array([0.64, 0.22, 0.62, 0.17])
bitcom = np.array([0.36, 0.75, 0.10, 0.37])
off = np.array([0.00, 0.03, 0.28, 0.46])
c_sem = '#f5b041'
c... | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as patches
labels = ['Case 1', 'Case 2', 'Case 3', 'Case 4']
x_pos = np.arange(len(labels))
semcom = np.array([0.64, 0.22, 0.62, 0.17])
bitcom = np.array([0.36, 0.75, 0.10, 0.37])
off = np.array([0.00, 0.03, 0.28, 0.46])
c_sem = '#f5b041'
c... | ||
Bar_58_intent_code_image | Bar | Bar_58 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.family'] = 'serif'
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
pumping = [490, 310, 355, 300, 185, 122, 176, 150, 192, 268, 350, 460]
demand = [480, 355, 340, 300, 178, 155, 160, 152, 178, 250, 300, ... | import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.family'] = 'serif'
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
pumping = [490, 310, 355, 300, 185, 122, 176, 150, 192, 268, 350, 460]
demand = [480, 355, 340, 300, 178, 155, 160, 152, 178, 250, 300, ... | ||
Bar_59_intent_code_image | Bar | Bar_59 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
years = [f"{2023+i}-{str(2024+i)[-2:]}" for i in range(27)]
categories = [
"Black Coal", "Brown Coal", "Mid-merit Gas",
"Peaking Gas & Liquids", "Hydro", "Utility-scale Storage",
"Coordinated DER Storage",
"Distributed Storage",
... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
years = [f"{2023+i}-{str(2024+i)[-2:]}" for i in range(27)]
categories = [
"Black Coal", "Brown Coal", "Mid-merit Gas",
"Peaking Gas & Liquids", "Hydro", "Utility-scale Storage",
"Coordinated DER Storage",
"Distributed Storage",
... | ||
Bar_60_intent_code_image | Bar | Bar_60 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
years = list(range(1994, 2021))
data = [
72000, 55000, 58000, 60000, 65000, 92000, 83000, 8000, 75000, 80000,
135000, 105000, 165000, 195000, 160000, 125000, 125000, 135000, 155000,
210000, 225000, 185000, 202000, 330000, 260000, 165000,... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
years = list(range(1994, 2021))
data = [
72000, 55000, 58000, 60000, 65000, 92000, 83000, 8000, 75000, 80000,
135000, 105000, 165000, 195000, 160000, 125000, 125000, 135000, 155000,
210000, 225000, 185000, 202000, 330000, 260000, 165000,... | ||
Bar_61_intent_code_image | Bar | Bar_61 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Rectangle
years = ['1996', '2002', '2004', '2014', 'Oct. 6, 2017']
categories = ['All', 'D', 'I', 'R']
labels = ['A', 'D', 'I', 'R']
proud_data = [
[79, 75, 85, 70],
[90, 90, 90, 90],
[84, 85, 85, 80],
[74, 70, 75, 70],
... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Rectangle
years = ['1996', '2002', '2004', '2014', 'Oct. 6, 2017']
categories = ['All', 'D', 'I', 'R']
labels = ['A', 'D', 'I', 'R']
proud_data = [
[79, 75, 85, 70],
[90, 90, 90, 90],
[84, 85, 85, 80],
[74, 70, 75, 70],
... | ||
Bar_62_intent_code_image | Bar | Bar_62 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 6))
years = [1986, 1991, 1996, 2001, 2006, 2011, 2016]
percentages = [4.0, 5.2, 5.3, 5.2, 7.5, 7.7, 8.2]
bars = ax.bar(years, percentages, width=2.5, color='#1f77b4', edgecolor='none')
ax.set_ylim(0, 9)
ax.set_yticks(np.arange(0, 9.1... | import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 6))
years = [1986, 1991, 1996, 2001, 2006, 2011, 2016]
percentages = [4.0, 5.2, 5.3, 5.2, 7.5, 7.7, 8.2]
bars = ax.bar(years, percentages, width=2.5, color='#1f77b4', edgecolor='none')
ax.set_ylim(0, 9)
ax.set_yticks(np.arange(0, 9.1... | ||
Bar_63_intent_code_image | Bar | Bar_63 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
nations = [
"New Zealand", "Australia", "India", "Sri Lanka", "Zimbabwe",
"UAE", "England", "Ireland", "Bangladesh", "South Africa", "West Indies"
]
averages = [
36.56, 36.07, 33.37, 32.19, 31.97,
28.14, 27.81, 27.61, 27.60, 26.42, 25.26
]
data = sorted(zip(averages, nati... | import matplotlib.pyplot as plt
nations = [
"New Zealand", "Australia", "India", "Sri Lanka", "Zimbabwe",
"UAE", "England", "Ireland", "Bangladesh", "South Africa", "West Indies"
]
averages = [
36.56, 36.07, 33.37, 32.19, 31.97,
28.14, 27.81, 27.61, 27.60, 26.42, 25.26
]
data = sorted(zip(averages, nati... | ||
Bar_64_intent_code_image | Bar | Bar_64 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import matplotlib.patches as patches
fig, ax = plt.subplots(figsize=(12, 7))
categories = ['F-35', 'Eurofighter*', 'Rafale', 'F-18 E/F', 'F-16', 'JAS 39']
heights = [0, 8200, 16500, 11000, 7700, 4700]
colors = {
'F-35': 'grey',
'Eurofighter*': '#D4AF37',
'Rafale': '#59594A',
... | import matplotlib.pyplot as plt
import matplotlib.patches as patches
import textwrap
fig, ax = plt.subplots(figsize=(12, 7))
categories = ['F-35', 'Eurofighter*', 'Rafale', 'F-18 E/F', 'F-16', 'JAS 39']
heights = [0, 8200, 16500, 11000, 7700, 4700]
colors = {
'F-35': 'grey',
'Eurofighter*': '#D4AF37',
'Rafa... | ||
Bar_65_intent_code_image | Bar | Bar_65 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(8, 6))
groups = ['Net Treasury Securities', 'Net Federal Agency & Gov\'t-Sponsored Enterprise MBS']
years = ['2019', '2020']
data = {
'2019': [2.40, 1.45],
'2020': [4.96, 2.11]
}
color_2019 = '#2E9FD6'
color_2020 = '#1E3A50'
bar_... | import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(8, 6))
groups = ['Net Treasury Securities', 'Net Federal Agency & Gov\'t-Sponsored Enterprise MBS']
years = ['2019', '2020']
data = {
'2019': [2.40, 1.45],
'2020': [4.96, 2.11]
}
color_2019 = '#2E9FD6'
color_2020 = '#1E3A50'
bar_... | ||
Bar_66_intent_code_image | Bar | Bar_66 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.lines import Line2D
import matplotlib.patches as patches
years = ['1990', '2000', '2010']
categories = [
'One person, nonfamily',
'Two or more people, nonfamily',
'Male householder, other family',
'Female householder, other family',
... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.lines import Line2D
import matplotlib.patches as patches
years = ['1990', '2000', '2010']
categories = [
'One person, nonfamily',
'Two or more people, nonfamily',
'Male householder, other family',
'Female householder, other family',
... | ||
Bar_67_intent_code_image | Bar | Bar_67 | intent | Input: code+Image | You are an expert in chart annotation and Python visualization. I have created a figure but have not added any annotations yet. I will provide you with the chart code without annotations, along with its corresponding reference image and annotation instructions. The image is generated by the code and is provided to help... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Patch
plt.figure(figsize=(12, 7))
plt.style.use('seaborn-v0_8-whitegrid')
categories = ['Excellent', 'Very good', 'Good', 'Fair/poor']
years = [2008, 2010, 2012, 2014, 2016, 2018]
colors = ['#004488', '#CCAA00', '#BB2222', '#440066', '#44... | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Patch
plt.figure(figsize=(12, 7))
plt.style.use('seaborn-v0_8-whitegrid')
categories = ['Excellent', 'Very good', 'Good', 'Fair/poor']
years = [2008, 2010, 2012, 2014, 2016, 2018]
colors = ['#004488', '#CCAA00', '#BB2222', '#440066', '#44... |
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