Python display specific values on x-axis using matplotlib - python

I'm querying data from a simple sqlite3 DB which is pulling a list of the number of connections per port observed on my system. I'm trying to graph this into a simple bar-chart using matplotlib.
Thus far, I'm using the follow code:
import matplotlib as mpl
mpl.use('Agg') # force no x11
import matplotlib.pyplot as plt
import sqlite3
con = sqlite3.connect('test.db')
cur = con.cursor()
cur.execute('''
SELECT dst_port, count(dst_port) as count from logs
where dst_port != 0
group by dst_port
order by count desc;
'''
)
data = cur.fetchall()
dst_ports, dst_port_count = zip(*data)
#dst_ports = [22, 53223, 40959, 80, 3389, 23, 443, 35829, 8080, 4899, 21320, 445, 3128, 44783, 4491, 9981, 8001, 21, 1080, 8081, 3306, 8002, 8090]
#dst_port_count = [5005, 145, 117, 41, 34, 21, 17, 16, 15, 11, 11, 8, 8, 8, 6, 6, 4, 3, 3, 3, 1, 1, 1]
print dst_ports
print dst_port_count
fig = plt.figure()
# aesthetics and data
plt.grid()
plt.bar(dst_ports, dst_port_count, align='center')
#plt.xticks(dst_ports)
# labels
plt.title('Number of connections to port')
plt.xlabel('Destination Port')
plt.ylabel('Connection Attempts')
# save figure
fig.savefig('temp.png')
When I run the above, the data is successful retrieved from the DB and a graph is generated. However, the graph isn't what I was expecting. For example, on the x-axis, it plots all values between 0 and 5005. I'm looking for it to display only the values in dst_ports. I've tried using xticks but this doesn't work either.
I've included some sample data in the above code which I've commented out that may be useful.
In addition, here is an example of the graph output from the above code:
And also a grpah when using xticks:


You need to create some xdata by np.arange():
import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
dst_ports = [22, 53223, 40959, 80, 3389, 23, 443, 35829, 8080, 4899, 21320, 445, 3128, 44783, 4491, 9981, 8001, 21, 1080, 8081, 3306, 8002, 8090]
dst_port_count = [5005, 145, 117, 41, 34, 21, 17, 16, 15, 11, 11, 8, 8, 8, 6, 6, 4, 3, 3, 3, 1, 1, 1]
fig = plt.figure(figsize=(12, 4))
# aesthetics and data
plt.grid()
x = np.arange(1, len(dst_ports)+1)
plt.bar(x, dst_port_count, align='center')
plt.xticks(x, dst_ports, rotation=45)
# labels
plt.title('Number of connections to port')
plt.xlabel('Destination Port')
plt.ylabel('Connection Attempts')
Here is the output:

Related

Adding labels to seaborn bars

I'm trying to create two, vertically aligned, horizontal grouped bar charts. I have a huge amount of data for several Machine Learning models and their corresponding runtimes and would like to display all this data in a meaningful way. My attempt so far looks as follows:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
labels = ['MLP','FCN','ResNet','ROCKET','1-NN DTW','LightGBM','XGBoost','CatBoost']
Data1_Accuracy = [20, 34, 30, 35, 27,77.83125,78.7204167,78.5354167]
Data2_Accuracy = [20, 34, 30, 35, 27,75.7979167,76.2520833,77.87]
Data3_Accuracy = [20, 34, 30, 35, 27,80.14625,81.5033333,81.4625]
Data4_Accuracy = [20, 34, 30, 35, 27,78.3841667,79.34875,80.5270833]
Data5_Accuracy = [20, 34, 30, 35, 27,79.2495833,77.5370833,79.2666667]
Data6_Accuracy = [20, 34, 30, 35, 27,77.03125,77.2429167,77.9960275]
Data7_Accuracy = [20, 34, 30, 35, 27,81.3241667,80.5408333,84.2083333]
Data8_Accuracy = [20, 34, 30, 35, 27,78.1470833,78.1225,80.2754167]
Data9_Accuracy = [20, 34, 30, 35, 27,80.7383333,79.9358333,79.6916667]
Data10_Accuracy = [20, 34, 30, 35, 27,74.1095833,73.0879167,73.0529167]
Data11_Accuracy = [20, 34, 30, 35, 27,78.4775,77.8658333,78.35]
Data12_Accuracy = [20, 34, 30, 35, 27,73.0991667,71.9683333,72.75625]
Data13_Accuracy = [20, 34, 30, 35, 27,79.03,79.575,80.3870833]
Data14_Accuracy = [20, 34, 30, 35, 27,81.0241667,81.455,80.5516667]
Data15_Accuracy = [20, 34, 30, 35, 27,79.4829167,80.01375,81.68]
Data16_Accuracy = [20, 34, 30, 35, 27,81.1158333,80.9795833,80.6541667]
Data1_Times = [20, 34, 30, 35, 27,829.0177925,58.6558111,8493.968922]
Data2_Times = [20, 34, 30, 35, 27,604.5935536,64.3871907,6833.585728]
Data3_Times = [20, 34, 30, 35, 27,1286.01507,92.4329714,6821.308612]
Data4_Times = [20, 34, 30, 35, 27,757.3903304,78.7253731,5455.483287]
Data5_Times = [20, 34, 30, 35, 27,401.3722335,30.4119882,5160.041989]
Data6_Times = [20, 34, 30, 35, 27,321.4673242,54.1971346,4465.557807]
Data7_Times = [20, 34, 30, 35, 27,2598.48826,193.1256487,10811.65574]
Data8_Times = [20, 34, 30, 35, 27,1545.059628,139.9638344,7784.332016]
Data9_Times = [20, 34, 30, 35, 27,663.416329,615.3660963,3560.337827]
Data10_Times = [20, 34, 30, 35, 27,670.1615828,621.8249994,3567.653313]
Data11_Times = [20, 34, 30, 35, 27,619.1959161,572.3292757,3493.582855]
Data12_Times = [20, 34, 30, 35, 27,626.107683,579.0746278,3528.605614]
Data13_Times = [20, 34, 30, 35, 27,2936.5633,2631.284413,6465.254111]
Data14_Times = [20, 34, 30, 35, 27,2967.02757,2672.068268,6551.57865]
Data15_Times = [20, 34, 30, 35, 27,4102.511475,3711.899848,7704.401239]
Data16_Times = [20, 34, 30, 35, 27,4075.485739,3726.896591,7737.482708]
Data1_TimesInHours = np.array(Data1_Times) / 3600
Data2_TimesInHours = np.array(Data2_Times) / 3600
Data3_TimesInHours = np.array(Data3_Times) / 3600
Data4_TimesInHours = np.array(Data4_Times) / 3600
Data5_TimesInHours = np.array(Data5_Times) / 3600
Data6_TimesInHours = np.array(Data6_Times) / 3600
Data7_TimesInHours = np.array(Data7_Times) / 3600
Data8_TimesInHours = np.array(Data8_Times) / 3600
Data9_TimesInHours = np.array(Data9_Times) / 3600
Data10_TimesInHours = np.array(Data10_Times) / 3600
Data11_TimesInHours = np.array(Data11_Times) / 3600
Data12_TimesInHours = np.array(Data12_Times) / 3600
Data13_TimesInHours = np.array(Data13_Times) / 3600
Data14_TimesInHours = np.array(Data14_Times) / 3600
Data15_TimesInHours = np.array(Data15_Times) / 3600
Data16_TimesInHours = np.array(Data16_Times) / 3600
accuraciesDataFrame = pd.DataFrame({'Index': labels,
'Data1_Accuracy': Data1_Accuracy,
'Data2_Accuracy': Data2_Accuracy,
'Data3_Accuracy': Data3_Accuracy,
'Data4_Accuracy': Data4_Accuracy,
'Data5_Accuracy': Data5_Accuracy,
'Data6_Accuracy': Data6_Accuracy,
'Data7_Accuracy': Data7_Accuracy,
'Data8_Accuracy': Data8_Accuracy,
'Data9_Accuracy': Data9_Accuracy,
'Data10_Accuracy': Data10_Accuracy,
'Data11_Accuracy': Data11_Accuracy,
'Data12_Accuracy)': Data12_Accuracy,
'Data13_Accuracy': Data13_Accuracy,
'Data14_Accuracy': Data14_Accuracy,
'Data15_Accuracy': Data15_Accuracy,
'Data16_Accuracy': Data16_Accuracy},
columns = ['Index','Data1_Accuracy','Data2_Accuracy','Data3_Accuracy','Data4_Accuracy','Data5_Accuracy','Data6_Accuracy','Data7_Accuracy','Data8_Accuracy','Data9_Accuracy','Data10_Accuracy',
'Data11_Accuracy','Data12_Accuracy','Data13_Accuracy','Data14_Accuracy','Data15_Accuracy','Data16_Accuracy'])
timesDataFrame = pd.DataFrame({'Index': labels,
'Data1_TimesInHours': Data1_TimesInHours,
'Data2_TimesInHours': Data2_TimesInHours,
'Data3_TimesInHours': Data3_TimesInHours,
'Data4_TimesInHours': Data4_TimesInHours,
'Data5_TimesInHours': Data5_TimesInHours,
'Data6_TimesInHours': Data6_TimesInHours,
'Data7_TimesInHours': Data7_TimesInHours,
'Data8_TimesInHours': Data8_TimesInHours,
'Data9_TimesInHours': Data9_TimesInHours,
'Data10_TimesInHours': Data10_TimesInHours,
'Data11_TimesInHours': Data11_TimesInHours,
'Data12_TimesInHours': Data12_TimesInHours,
'Data13_TimesInHours': Data13_TimesInHours,
'Data14_TimesInHours': Data14_TimesInHours,
'Data15_TimesInHours': Data15_TimesInHours,
'Data16_TimesInHours': Data16_TimesInHours},
columns = [
'Index','Data1_TimesInHours','Data2_TimesInHours','Data3_TimesInHours','Data4_TimesInHours',
'Data5_TimesInHours','Data6_TimesInHours','Data7_TimesInHours','Data8_TimesInHours','Data9_TimesInHours','Data10_TimesInHours',
'Data11_TimesInHours','Data12_TimesInHours','Data13_TimesInHours','Data14_TimesInHours','Data15_TimesInHours','Data16_TimesInHours'
])
accuraciesDataFrameMelted = pd.melt(accuraciesDataFrame, id_vars=['Index'])
timesDataFrameMelted = pd.melt(timesDataFrame, id_vars=['Index'])
fig, axs = plt.subplots(1,2)
fig.set_size_inches(30,10)
xRangeFirstChart = list(range(0,101))
fig.suptitle('Rounded accuracies (%) and times for training and evaluation (h) for different data types and models',fontsize=26)
g1 = sns.barplot(x='value', y='Index', hue='variable', data=accuraciesDataFrameMelted, ax=axs[0])
axs[0].set_xlim([xRangeFirstChart[0],xRangeFirstChart[-1]])
axs[0].set_ylabel('Model',fontsize=24)
axs[0].set_xlabel('Rounded Accuracy (%)',fontsize=24)
axs[0].set_title('Rounded accuracies (%) for different data types and models',fontsize=22)
g2 = sns.barplot(x='value', y='Index', hue='variable', data=timesDataFrameMelted, ax=axs[1])
axs[0].get_legend().remove()
axs[1].get_legend().remove()
axs[1].get_yaxis().set_visible(False)
axs[1].set_xlabel('Training and evaluation time (h)',fontsize=24)
axs[1].set_title('Rounded training and evaluation time (h) for different data types and models',fontsize=22)
plt.savefig('PathToFigure/MyFigure.png', dpi=300, bbox_inches='tight', pad_inches=0)
What I'm missing is a way to write the labels "Data 1", "Data 2", Data 3", etc... in every bar. Please refer to the image for a visualization of what I'm trying to achieve. Any help is highly appreciated!

Since there are so many bars in one graph, I would use sns.catplot to draw the the different categories into a Facet Grid and then it would be much better for adding labels, which you can do with the custom function add_labels (please note the different parameters -- feel free to remove some/add others. I have adapted from this solution).
You could also make the x-axis more variable if you pass sharex=False when creating the catplots (see end of this solution)
Also, sns.catplot doesn't work well with adding to subplots, so that you can save as one figure. This is why I use plt.close(fig) to get rid of the blank figure we created, and this would also mean adding any formatting (such as adding a title) to that figure would be pointless, since we are getting rid of the figure at the end; however, there are hacks. One is to save as separate figures and use a solution from here: to combine into one .pdf. I think it would be better to have the extra space of one graph per page or image. Another option is to use somewhat of a hack to get into one figure:
fig, ax = plt.subplots(nrows=2)
sns.set_context('paper', font_scale=1.4)
plt.style.use('dark_background')
n_cols=4 #this is used later in a couple of places to make dynamic
g1 = sns.catplot(data=accuraciesDataFrameMelted, x='value', y='variable', col='Index', kind='bar',
col_wrap=n_cols, ax=ax[0])
g1.fig.suptitle('Rounded accuracies (%) for different data types and models',fontsize=22)
plt.subplots_adjust(top=0.9, bottom=-0.5)
g2 = sns.catplot(data=timesDataFrameMelted, x='value', y='variable', col='Index', kind='bar',
col_wrap=n_cols, ax=ax[1])
g2.fig.suptitle('Rounded training and evaluation time (h) for different data types and models',fontsize=22)
plt.subplots_adjust(top=0.9, bottom=-0.5)
def add_labels(graph, category_size, axis_number, omit_thresh, width_var, num_format):
for i in range(category_size):
ax = graph.facet_axis(axis_number,i)
for p in ax.patches:
if p.get_width() > omit_thresh: # omit labels close to zero or other threshold
width = p.get_width() * width_var # get bar length
ax.text(width, # set the text at 1 unit right of the bar
p.get_y() + p.get_height() / 2, # get Y coordinate + X coordinate / 2
num_format.format(p.get_width()), # set variable to display, 2 decimals
ha = 'center', # horizontal alignment
va = 'center') # vertical alignment
else:
pass
l1 = len(accuraciesDataFrameMelted['Index'].unique())
l2 = len(timesDataFrame['Index'].unique())
add_labels(graph=g1, category_size=l1, axis_number=0, omit_thresh=1, width_var=0.5, num_format='{:1.0f}')
add_labels(graph=g2, category_size=l2, axis_number=1, omit_thresh=0.1, width_var=0.5, num_format='{:1.2f}')
for g, i in zip([g1,g2], [0, n_cols]):
g.axes[i].set_ylabel('Model')
for g in [g1,g2]:
g.set_titles("{col_name}", fontsize=12)
g1.set_axis_labels(x_var="Rounded Accuracy (%)", y_var="Model")
g2.set_axis_labels(x_var="Training and evaluation time (h)", y_var="Model")
plt.close(fig)
g1.fig.savefig('g1.pdf',dpi=300, bbox_inches = "tight")
g2.fig.savefig('g2.pdf',dpi=300, bbox_inches = "tight")
plt.show()
(Zoomed In to show first graph)
(Zoomed Out to show both graphs)
You could also make the x-axis more variable if you pass sharex=False when creating the catplot, by making the changes below (pass sharex and change one of the params in my function to `omit_thresh=0:
g1 = sns.catplot(data=accuraciesDataFrameMelted, x='value', y='variable',
col='Index', kind='bar',
col_wrap=n_cols, ax=ax[0], sharex=False)
g2 = sns.catplot(data=timesDataFrameMelted, x='value', y='variable', col='Index', kind='bar',
col_wrap=n_cols, ax=ax[1], sharex=False)
add_labels(graph=g1, category_size=l1, axis_number=0, omit_thresh=0, width_var=0.5, num_format='{:1.0f}')
add_labels(graph=g2, category_size=l2, axis_number=1, omit_thresh=0, width_var=0.5, num_format='{:1.3f}')

Multi-layer Plot in plolty

Can we plot another layer of plot over a visualization using plotly through python.?
I am trying to join two points over a bubble chart.

You should be aware of the concept of traces, where we can place two types of charts in the same graph. To see the concept in action refer Multiple Chart Types in Same Graph. Just as a starting point please refer the below code snippet which does what you want.
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objs as go
from datetime import datetime
init_notebook_mode(connected=True)
trace0 = go.Scatter(
x=[1, 2, 3, 4],
y=[10, 11, 12, 13],
mode='markers',
marker=dict(
size=[40, 60, 80, 100],
)
)
trace1 = go.Scatter(
x = [1, 2, 3, 4],
y = [10, 11, 12, 13],
line = dict(
color = ('rgb(22, 96, 167)'),
width = 4,
dash = 'line')
)
data = [trace0, trace1]
iplot(data, filename='bubblechart-size')
The line that you require can also be generated using plotly shapes. Please read more about this here

Cluster datapoints using kmeans sklearn in python

I am using the following python code to cluster my datapoints using kmeans.
data = np.array([[30, 17, 10, 32, 32], [18, 20, 6, 20, 15], [10, 8, 10, 20, 21], [3, 16, 20, 10, 17], [3, 15, 21, 17, 20]])
kmeans_clustering = KMeans( n_clusters = 3 )
idx = kmeans_clustering.fit_predict( data )
#use t-sne
X = TSNE(n_components=2).fit_transform( data )
fig = plt.figure(1)
plt.clf()
#plot graph
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
plt.scatter(X[:,0], X[:,1], c=colors[kmeans_clustering.labels_])
plt.title('K-Means (t-SNE)')
plt.show()
However, the plot of the clusters I get is wrong as I get everything in one point.
Hence, please let me know where I am making my code wrong? I want to view the kmeans clusters seperately in my scatter plot.
EDIT
The t-sne vales I get are as follows.
[[ 1.12758575e-04 9.30458337e-05]
[ -1.82559784e-04 -1.06657936e-04]
[ -9.56485652e-05 -2.38951623e-04]
[ 5.56515580e-05 -4.42453191e-07]
[ -1.42039677e-04 -5.62548119e-05]]

Use the perplexity parameter of the TSNE. The default value of the perplexity is 30, it seems that's too much for your case, even though the documentation states that TSNE is quite insensitive to this parameter.
The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity. Consider selecting a value between 5 and 50. The choice is not extremely critical since t-SNE is quite insensitive to this parameter.
X = TSNE(n_components=2, perplexity=2.0).fit_transform( data )

You could also use PCA (Principal Components Analysis) instead of t-SNE to plot your clusters:
import numpy as np
import pandas as pd
from sklearn.cluster import Kmeans
from sklearn.decomposition import PCA
data = np.array([[30, 17, 10, 32, 32], [18, 20, 6, 20, 15], [10, 8, 10, 20,
21], [3, 16, 20, 10, 17], [3, 15, 21, 17, 20]])
kmeans = KMeans(n_clusters = 3)
labels = kmeans.fit_predict(data)
pca = PCA(n_components=2)
data_reduced = pca.fit_transform(data)
data_reduced = pd.DataFrame(data_reduced)
ax = data_reduced.plot(kind='scatter', x=0, y=1, c=labels, cmap='rainbow')
ax.set_xlabel('PC1')
ax.set_ylabel('PC2')
ax.set_title('Projection of the clustering on a the axis of the PCA')
for x, y, label in zip(data_reduced[0], data_reduced[1], kmeans.labels_):
ax.annotate('Cluster {0}'.format(label), (x,y))

How to remove empty "padding" in matplotlib barh plot?

I want to remove/reduce the empty top and bottom padding space (marked with red squares) in the matplotlib.pyplot.barh plot. How can I do it?
Here is an example of my plot:
Here is the code:
import matplotlib.pyplot as plt
from collections import Counter
import random
values = sorted(tags_dic.values())
labels = sorted(tags_dic, key=tags_dic.get)
bars = plt.barh(range(len(tags_dic)), values, align='center')
plt.yticks(range(len(tags_dic)), labels)
plt.xlabel('Count')
plt.ylabel('POS-tags')
plt.grid(True)
random.shuffle(COLLECTION)
for i in range(len(tags_dic)):
bars[i].set_color(COLLECTION[i])
print COLLECTION[i]
plt.show()
Random test data:
tags_dic = Counter({u'NNP': 521, u'NN': 458, u'IN': 450, u'DT': 415, u'JJ': 268, u'NNS': 244, u'VBD': 144, u'CC': 138, u'RB': 103, u'VBN': 98, u'VBZ': 69, u'VB': 65, u'TO': 64, u'PRP': 57, u'CD': 51, u'VBG': 50, u'VBP': 48, u'PRP$': 26, u'POS': 26, u'WDT': 20, u'WP': 20, u'MD': 19, u'EX': 11, u'WRB': 10, u'JJS': 7, u'RP': 6, u'JJR': 6, u'RBR': 5, u'NNPS': 5, u'FW': 4, u'SYM': 1, u'UH': 1})

You can control this with plt.margins. To completely remove the whitespace at the top and bottom, you can set:
plt.margins(y=0)
As an aside, I think you also have an error in your plotting script: you sort the values from your dictionary, but not the keys, so you end up with labels that don't correspond to the value they represent.
I think you can fix this as follows:
labels = sorted(tags_dic, key=tags_dic.get)
plt.yticks(range(len(tags_dic)), labels)

Financial Charts / Graphs in Ruby or Python

What are my best options for creating a financial open-high-low-close (OHLC) chart in a high level language like Ruby or Python? While there seem to be a lot of options for graphing, I haven't seen any gems or eggs with this kind of chart.
http://en.wikipedia.org/wiki/Open-high-low-close_chart (but I don't need the moving average or Bollinger bands)
JFreeChart can do this in Java, but I'd like to make my codebase as small and simple as possible.
Thanks!

You can use matplotlib and the the optional bottom parameter of matplotlib.pyplot.bar. You can then use line plot to indicate the opening and closing prices:
For example:
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import lines
import random
deltas = [4, 6, 13, 18, 15, 14, 10, 13, 9, 6, 15, 9, 6, 1, 1, 2, 4, 4, 4, 4, 10, 11, 16, 17, 12, 10, 12, 15, 17, 16, 11, 10, 9, 9, 7, 10, 7, 16, 8, 12, 10, 14, 10, 15, 15, 16, 12, 8, 15, 16]
bases = [46, 49, 45, 45, 44, 49, 51, 52, 56, 58, 53, 57, 62, 63, 68, 66, 65, 66, 63, 63, 62, 61, 61, 57, 61, 64, 63, 58, 56, 56, 56, 60, 59, 54, 57, 54, 54, 50, 53, 51, 48, 43, 42, 38, 37, 39, 44, 49, 47, 43]
def rand_pt(bases, deltas):
return [random.randint(base, base + delta) for base, delta in zip(bases, deltas)]
# randomly assign opening and closing prices
openings = rand_pt(bases, deltas)
closings = rand_pt(bases, deltas)
# First we draw the bars which show the high and low prices
# bottom holds the low price while deltas holds the difference
# between high and low.
width = 0
ax = plt.axes()
rects1 = ax.bar(np.arange(50), deltas, width, color='r', bottom=bases)
# Now draw the ticks indicating the opening and closing price
for opening, closing, bar in zip(openings, closings, rects1):
x, w = bar.get_x(), 0.2
args = {
}
ax.plot((x - w, x), (opening, opening), **args)
ax.plot((x, x + w), (closing, closing), **args)
plt.show()
creates a plot like this:
Obviously, you'd want to package this up in a function that drew the plot using (open, close, min, max) tuples (and you probably wouldn't want to randomly assign your opening and closing prices).

You can use Pylab (matplotlib.finance) with Python. Here are some examples: http://matplotlib.sourceforge.net/examples/pylab_examples/plotfile_demo.html . There is some good material specifically on this problem in Beginning Python Visualization.
Update: I think you can use matplotlib.finance.candlestick for the Japanese candlestick effect.

Have you considered using R and the quantmod package? It likely provides exactly what you need.

Some examples about financial plots (OHLC) using matplotlib can be found here:
finance demo
#!/usr/bin/env python
from pylab import *
from matplotlib.dates import DateFormatter, WeekdayLocator, HourLocator, \
DayLocator, MONDAY
from matplotlib.finance import quotes_historical_yahoo, candlestick,\
plot_day_summary, candlestick2
# (Year, month, day) tuples suffice as args for quotes_historical_yahoo
date1 = ( 2004, 2, 1)
date2 = ( 2004, 4, 12 )
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d') # Eg, Jan 12
dayFormatter = DateFormatter('%d') # Eg, 12
quotes = quotes_historical_yahoo('INTC', date1, date2)
if len(quotes) == 0:
raise SystemExit
fig = figure()
fig.subplots_adjust(bottom=0.2)
ax = fig.add_subplot(111)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
#ax.xaxis.set_minor_formatter(dayFormatter)
#plot_day_summary(ax, quotes, ticksize=3)
candlestick(ax, quotes, width=0.6)
ax.xaxis_date()
ax.autoscale_view()
setp( gca().get_xticklabels(), rotation=45, horizontalalignment='right')
show()
finance work 2

Are you free to use JRuby instead of Ruby? That'd let you use JFreeChart, plus your code would still be in Ruby

Please look at the Open Flash Chart embedding for WHIFF
http://aaron.oirt.rutgers.edu/myapp/docs/W1100_1600.openFlashCharts
An example of a candle chart is right at the top. This would be especially
good for embedding in web pages.

Open Flash Chart is nice choice if you like the look of examples. I've moved to JavaScript/Canvas library like Flot for HTML embedded charts, as it is more customizable and I get desired effect without much hacking (http://itprolife.worona.eu/2009/08/scatter-chart-library-moving-to-flot.html).

This is the stock chart I draw just days ago using Matplotlib, I've posted the source too, for your reference: StockChart_Matplotlib

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