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This article is reproduced from the I CSDN blog: http://blog.csdn.net/ctojxzsycztao/archive//4169237.aspx First of all, SPRING and HIBERNATE need to prepare for the JAR corresponding package in the ECLIPSE project to create a new WEB, SPRIN
From Address: https: / / /developerworks/cn/java/j-lo-spring25-mvc/ A simple annotation-based Controller The use of low version of the Spring MVC readers know: When you create a Controller, we need to directly or indirectly, to achieve org
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The parameters are as follows @ RequestMapping /** * @see RequestMapping Parameters * @param value * The address you want to jump * @param mehtod * Jump parameters based on RestFul , RequestMethod.get post,put * @param params * In line with a paramet
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processed in 0.049 (s). 9 q(s)matplotlib基本使用方法示例 - JerryWisdom的博客 - CSDN博客
matplotlib基本使用方法示例
Deep Learning
这里是用jupyter notebook写的matplotlib的基本用法，使用的环境是python3+windows，代码上传到csdn资源啦：
关于matplotlib学习还是强烈建议常去官方里查一查各种用法和toturial等。&
下面是jupyter notebook代码导出的md文件。
Plotting and Visualization
from __future__ import division
from numpy.random import randn
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(12345)
plt.rc('figure', figsize=(10, 6))
from pandas import Series, DataFrame
import pandas as pd
np.set_printoptions(precision=4)12345678910
%matplotlib inline1
matplotlib API 介绍
import matplotlib.pyplot as plt1
Figures and Subplots
fig = plt.figure()1
ax1 = fig.add_subplot(2, 2, 1)1
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2, 3)12
from numpy.random import randn
plt.plot(randn(50).cumsum(), 'k--')12
[&matplotlib.lines.Line2D at 0x28e7668cb38&]
_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)
ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))12
plt.close('all')1
fig, axes = plt.subplots(2, 3)
array([[&matplotlib.axes._subplots.AxesSubplot object at 0xBAFF98&,
&matplotlib.axes._subplots.AxesSubplot object at 0xC047F0&,
&matplotlib.axes._subplots.AxesSubplot object at 0xC4CB00&],
[&matplotlib.axes._subplots.AxesSubplot object at 0xC89D30&,
&matplotlib.axes._subplots.AxesSubplot object at 0xCD7940&,
&matplotlib.axes._subplots.AxesSubplot object at 0xD0FFD0&]], dtype=object)
## 调整subplot间距
plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=None, hspace=None)12
fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)
for i in range(2):
for j in range(2):
axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)
plt.subplots_adjust(wspace=0, hspace=0)12345
fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)
for i in range(2):
for j in range(2):
axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)
plt.subplots_adjust(wspace=0, hspace=0)12345
### 线条格式
plt.figure()1
plt.plot(randn(30).cumsum(), 'ko--')1
[&matplotlib.lines.Line2D at 0x28e&]
plt.close('all')1
data = randn(30).cumsum()
plt.plot(data, 'k--', label='Default')
plt.plot(data, 'k-', drawstyle='steps-post', label='steps')
plt.legend(loc='best')1234
&matplotlib.legend.Legend at 0x28e&
### Ticks, labels, and legends #### Setting the title, axis labels, ticks, and ticklabels
fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)
ax.plot(randn(1000).cumsum())
ticks = ax.set_xticks([0, 250, 500, 750, 1000])
labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],
rotation=30, fontsize='small')
ax.set_title('some random lines')
ax.set_xlabel('Stages')12345678
&matplotlib.text.Text at 0x28e&
#### Adding legends
fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)
ax.plot(randn(1000).cumsum(), 'k', label='one')
ax.plot(randn(1000).cumsum(), 'k--', label='two')
ax.plot(randn(1000).cumsum(), 'k.', label='three')
ax.legend(loc='best')123456
&matplotlib.legend.Legend at 0x28e&
### subplot 做标记
from datetime import datetime
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
data = pd.read_csv('julyedu/spx.csv', index_col=0, parse_dates=True)
spx = data['SPX']
spx.plot(ax=ax, style='k-')
crisis_data = [
(datetime(2007, 10, 11), 'Peak of bull market'),
(datetime(2008, 3, 12), 'Bear Stearns Fails'),
(datetime(2008, 9, 15), 'Lehman Bankruptcy')
for date, label in crisis_data:
ax.annotate(label, xy=(date, spx.asof(date) + 50),
xytext=(date, spx.asof(date) + 200),
arrowprops=dict(facecolor='black'),
horizontalalignment='left', verticalalignment='top')
ax.set_xlim(['1/1/2007', '1/1/2011'])
ax.set_ylim([600, 1800])
ax.set_title('Important dates in
financial crisis')123456789101112131415161718192021222324252627
&matplotlib.text.Text at 0x28e77fb7358&
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)
circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)
pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]],
color='g', alpha=0.5)
ax.add_patch(rect)
ax.add_patch(circ)
ax.add_patch(pgon)1234567891011
&matplotlib.patches.Polygon at 0x28e77ed76a0&
### Saving plots to file
fig.savefig('figpath.svg')1
fig.savefig('figpath.png', dpi=400, bbox_inches='tight')1
from io import BytesIO
buffer = BytesIO()
plt.savefig(buffer)
plot_data = buffer.getvalue()1234
### matplotlib configuration
plt.rc('figure', figsize=(10, 10))1
## Plotting functions in pandas ### Line plots
plt.close('all')1
s = Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))
s.plot()12
&matplotlib.axes._subplots.AxesSubplot at 0x28e781c0208&
df = DataFrame(np.random.randn(10, 4).cumsum(0),
columns=['A', 'B', 'C', 'D'],
index=np.arange(0, 100, 10))
df.plot()1234
&matplotlib.axes._subplots.AxesSubplot at 0x28e&
### Bar plots
fig, axes = plt.subplots(2, 1)
data = Series(np.random.rand(16), index=list('abcdefghijklmnop'))
data.plot(kind='bar', ax=axes[0], color='k', alpha=0.7)
data.plot(kind='barh', ax=axes[1], color='k', alpha=0.7)1234
&matplotlib.axes._subplots.AxesSubplot at 0x11fd02b50&
df = DataFrame(np.random.rand(6, 4),
index=['one', 'two', 'three', 'four', 'five', 'six'],
columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))
df.plot(kind='bar')12345
&matplotlib.axes._subplots.AxesSubplot at 0x28e77f482e8&
plt.figure()1
df.plot(kind='barh', stacked=True, alpha=0.5)1
&matplotlib.axes._subplots.AxesSubplot at 0x28e77e05be0&
tips = pd.read_csv('julyedu/tips.csv')
party_counts = pd.crosstab(tips.day, tips.size)
print(party_counts)
party_counts = party_counts.ix[:, 2:5]
print(party_counts)123456
col_0 1708 day Fri 19 Sat 87 Sun 76 Thur 62 Empty DataFrame Columns: [] Index: [Fri, Sat, Sun, Thur] ### Histograms and density plots
plt.figure()1
tips['tip_pct'] = tips['tip'] / tips['total_bill']
print(tips.head())
tips['tip_pct'].hist(bins=50)123
total_bill
sex smoker
&matplotlib.axes._subplots.AxesSubplot at 0x28e&
12345678910111213
plt.figure()1
tips['tip_pct'].plot(kind='kde')1
plt.figure()1
comp1 = np.random.normal(0, 1, size=200)
comp2 = np.random.normal(10, 2, size=200)
values = Series(np.concatenate([comp1, comp2]))
values.hist(bins=100, alpha=0.3, color='k', normed=True)
values.plot(kind='kde', style='k--')12345
&matplotlib.axes._subplots.AxesSubplot at 0x28e79b24358&
### Scatter plots
macro = pd.read_csv('julyedu/macrodata.csv')
data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]
trans_data = np.log(data).diff().dropna()
trans_data[-5:]1234
plt.figure()1
plt.scatter(trans_data['m1'], trans_data['unemp'])
plt.title('Changes in log %s vs. log %s' % ('m1', 'unemp'))12
&matplotlib.text.Text at 0x28e7bfebcc0&
pd.scatter_matrix(trans_data, diagonal='kde', alpha=0.3)1
array([[&matplotlib.axes._subplots.AxesSubplot object at 0xCA07EF0&,
&matplotlib.axes._subplots.AxesSubplot object at 0xC6E9128&,
&matplotlib.axes._subplots.AxesSubplot object at 0xDFEEBA8&,
&matplotlib.axes._subplots.AxesSubplot object at 0xC3DB3C8&],
[&matplotlib.axes._subplots.AxesSubplot object at 0xC9E5EB8&,
&matplotlib.axes._subplots.AxesSubplot object at 0xC9D0E10&,
&matplotlib.axes._subplots.AxesSubplot object at 0xBFE87B8&,
&matplotlib.axes._subplots.AxesSubplot object at 0xC732FD0&],
[&matplotlib.axes._subplots.AxesSubplot object at 0xC9704E0&,
&matplotlib.axes._subplots.AxesSubplot object at 0xCF63320&,
&matplotlib.axes._subplots.AxesSubplot object at 0xC8BB748&,
&matplotlib.axes._subplots.AxesSubplot object at 0xC820978&],
[&matplotlib.axes._subplots.AxesSubplot object at 0xC6BBB00&,
&matplotlib.axes._subplots.AxesSubplot object at 0xC3405F8&,
&matplotlib.axes._subplots.AxesSubplot object at 0xC874DA0&,
&matplotlib.axes._subplots.AxesSubplot object at 0xE036550&]], dtype=object)
1234567891011121314151617
## Plotting Maps: Visualizing Haiti Earthquake Crisis data
data = pd.read_csv('julyedu/Haiti.csv')
data[['INCIDENT DATE', 'LATITUDE', 'LONGITUDE']][:10]1
INCIDENT DATE
-72.533333
114.174287
-72.334671
-72.310079
-73.638800
-73.195000
-72.236841
-72.410010
data['CATEGORY'][:6]1
0 1. Urgences | Emergency, 3. Public Health, 1 1. Urgences | Emergency, 2. Urgences logistiqu… 2 2. Urgences logistiques | Vital Lines, 8. Autr… 3 1. Urgences | Emergency, 4 1.
Urgences | Emergency, 5 5e. Communication lines down, Name: CATEGORY, dtype: object
data.describe()1
-72.322680
-74.452757
-72.417500
-72.335000
-72.293570
114.174287
data = data[(data.LATITUDE & 18) & (data.LATITUDE & 20) &
(data.LONGITUDE & -75) & (data.LONGITUDE & -70)
& data.CATEGORY.notnull()]123
def to_cat_list(catstr):
stripped = (x.strip() for x in catstr.split(','))
return [x for x in stripped if x]
def get_all_categories(cat_series):
cat_sets = (set(to_cat_list(x)) for x in cat_series)
return sorted(set.union(*cat_sets))
def get_english(cat):
code, names = cat.split('.')
if '|' in names:
names = names.split(' | ')[1]
return code, names.strip()12345678910111213
get_english('2. Urgences logistiques | Vital Lines')1
('2', 'Vital Lines')
all_cats = get_all_categories(data.CATEGORY)
english_mapping = dict(get_english(x) for x in all_cats)
english_mapping['2a']
english_mapping['6c']12345
'Earthquake and aftershocks'
def get_code(seq):
return [x.split('.')[0] for x in seq if x]
all_codes = get_code(all_cats)
code_index = pd.Index(np.unique(all_codes))
dummy_frame = DataFrame(np.zeros((len(data), len(code_index))),
index=data.index, columns=code_index)1234567
dummy_frame.ix[:, :6].info()1
&class 'pandas.core.frame.DataFrame'&
Int64Index: 3569 entries, 0 to 3592
Data columns (total 6 columns):
3569 non-null float64
3569 non-null float64
3569 non-null float64
3569 non-null float64
3569 non-null float64
3569 non-null float64
dtypes: float64(6)
memory usage: 195.2 KB
123456789101112
for row, cat in zip(data.index, data.CATEGORY):
codes = get_code(to_cat_list(cat))
dummy_frame.ix[row, codes] = 1
data = data.join(dummy_frame.add_prefix('category_'))12345
data.ix[:, 10:15].info()1
&class 'pandas.core.frame.DataFrame'&
Int64Index: 3569 entries, 0 to 3592
Data columns (total 5 columns):
category_1
3569 non-null float64
category_1a
3569 non-null float64
category_1b
3569 non-null float64
category_1c
3569 non-null float64
category_1d
3569 non-null float64
dtypes: float64(5)
memory usage: 167.3 KB
1234567891011
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
def basic_haiti_map(ax=None, lllat=17.25, urlat=20.25,
lllon=-75, urlon=-71):
m = Basemap(ax=ax, projection='stere',
lon_0=(urlon + lllon) / 2,
lat_0=(urlat + lllat) / 2,
llcrnrlat=lllat, urcrnrlat=urlat,
llcrnrlon=lllon, urcrnrlon=urlon,
resolution='f')
m.drawcoastlines()
m.drawstates()
m.drawcountries()
return m1234567891011121314151617
---------------------------------------------------------------------------
ImportError
Traceback (most recent call last)
&ipython-input-66-ec31ba3e955e& in &module&()
----& 1 from mpl_toolkits.basemap import Basemap
2 import matplotlib.pyplot as plt
4 def basic_haiti_map(ax=None, lllat=17.25, urlat=20.25,
lllon=-75, urlon=-71):
ImportError: No module named 'mpl_toolkits.basemap'
1234567891011121314
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
to_plot = ['2a', '1', '3c', '7a']
lllat=17.25; urlat=20.25; lllon=-75; urlon=-71
for code, ax in zip(to_plot, axes.flat):
m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,
lllon=lllon, urlon=urlon)
cat_data = data[data['category_%s' % code] == 1]
x, y = m(cat_data.LONGITUDE.values, cat_data.LATITUDE.values)
m.plot(x, y, 'k.', alpha=0.5)
ax.set_title('%s: %s' % (code, english_mapping[code]))123456789101112131415161718
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
to_plot = ['2a', '1', '3c', '7a']
lllat=17.25; urlat=20.25; lllon=-75; urlon=-71
def make_plot():
for i, code in enumerate(to_plot):
cat_data = data[data['category_%s' % code] == 1]
lons, lats = cat_data.LONGITUDE, cat_data.LATITUDE
ax = axes.flat[i]
m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,
lllon=lllon, urlon=urlon)
x, y = m(lons.values, lats.values)
m.plot(x, y, 'k.', alpha=0.5)
ax.set_title('%s: %s' % (code, english_mapping[code]))
1234567891011121314151617181920212223
make_plot()
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