blocks|key|2618424|text|您可以创建为每个像素分配颜色代码的三维数组。所以如果你想要黑白的，你需要分别传递(0,0,0)和(1,1,1)。像这样的东西应该是有效的：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2618425|def+show_as_image(sample):
++++bitmap+=+sample.reshape((13,+8))
++++bitmap_colored+=+np.zeros((13,8,3))
++++bitmap_colored[bitmap>=0]+=+[1,1,1]+#+black+for+values+greater+or+equal+to+0
++++bitmap_colored[bitmap<0]+=+[0,0,0]+#+white+for+values+less+than+0
++++plt.figure()
++++plt.imshow(bitmap_colored,+interpolation='nearest')
++++plt.show()|code-block|syntax|javascript|2618426|例如：|2618427|>>>+sample+=+np.random.randint(low=-10,high=10,size=(1,104))
>>>+show_as_image(sample)|2618428|将输出如下所示的内容：|2618429|📷|atomic|2618430|​|2618431|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/518a41e9c94b12c801ad561dcc469cc3.png|imageAlt^0|14|7|1C|7|0|0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@$9|15|A|16|B|C]|$9|17|A|18|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|19|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1A|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|1B|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1C|8|@]|D|@]|E|$]]|$1|Q|3|R|5|S|7|1D|8|@]|D|@$9|1E|A|1F|1|1G]]|E|$]]|$1|T|3|U|5|6|7|1H|8|@]|D|@]|E|$]]|$1|V|3|-4|5|6|7|1I|8|@]|D|@]|E|$]]]|W|$X|$5|Y|Z|10|E|$11|12|13|-4]]]]

You can create a three-dimensional array that assigns a color code to each pixel. So if you want black and white, you will pass <code>(0,0,0)</code> and <code>(1,1,1)</code>, respectively. Something like this should work:

<pre><code>def show_as_image(sample):
 bitmap = sample.reshape((13, 8))
 bitmap_colored = np.zeros((13,8,3))
 bitmap_colored[bitmap&gt;=0] = [1,1,1] # black for values greater or equal to 0
 bitmap_colored[bitmap&lt;0] = [0,0,0] # white for values less than 0
 plt.figure()
 plt.imshow(bitmap_colored, interpolation='nearest')
 plt.show()
</code></pre>

For example:

<pre><code>&gt;&gt;&gt; sample = np.random.randint(low=-10,high=10,size=(1,104))
&gt;&gt;&gt; show_as_image(sample)
</code></pre>

will output something like this:
<a href="https://i.stack.imgur.com/e94To.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/e94To.png" alt="enter image description here"></a>

blocks|key|2618668|text|您可以设置颜色编码的规范化，使其在数据的负绝对值和正绝对值之间均匀分布。使用中间带有浅色的色彩映射表可以帮助可视化这些值离零有多远。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2618669|import+numpy+as+np
import+matplotlib.pyplot+as+plt

def+show_as_image(sample):
++++bitmap+=+sample.reshape((13,+8))
++++maxval+=+np.max(np.abs([bitmap.min(),bitmap.max()]))
++++plt.figure()
++++plt.imshow(bitmap,+cmap='RdYlGn',+interpolation='nearest',
+++++++++++++++vmin=-maxval,+vmax=maxval)
++++plt.colorbar()
++++plt.show()

sample=np.random.randn(1,104)
show_as_image(sample)|code-block|syntax|javascript|2618670|​|2618671|📷|atomic|offset|length|2618672|2618673|如果需要二进制映射，则可以将正值映射为1，将负值映射为0。|2618674|import+numpy+as+np
import+matplotlib.pyplot+as+plt

def+show_as_image(sample):
++++bitmap+=+sample.reshape((13,+8))
++++bitmap[bitmap+>=+0]+=+1
++++bitmap[bitmap+<+0]+=+0
++++plt.figure()
++++plt.imshow(bitmap,+cmap='RdYlGn',+interpolation='nearest',
+++++++++++++++vmin=-.1,+vmax=1.1)
++++plt.show()

sample=np.random.randn(1,104)
show_as_image(sample)|2618675|2618676|2618677|2618678|在这种情况下，色条的使用可能是无用的。|2618679|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/ce4b39b508c53830829720d67c6ec90e.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/9813c75618ef251677b0cf0f218ad0be.png^0|0|0|0|0|1|0|0|0|0|0|0|0|1|1|0|0|0^^$0|@$1|2|3|4|5|6|7|18|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|19|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|1A|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|1B|8|@]|9|@$L|1C|M|1D|1|1E]]|A|$]]|$1|N|3|H|5|6|7|1F|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|1G|8|@]|9|@]|A|$]]|$1|Q|3|R|5|D|7|1H|8|@]|9|@]|A|$E|F]]|$1|S|3|H|5|6|7|1I|8|@]|9|@]|A|$]]|$1|T|3|J|5|K|7|1J|8|@]|9|@$L|1K|M|1L|1|1M]]|A|$]]|$1|U|3|H|5|6|7|1N|8|@]|9|@]|A|$]]|$1|V|3|W|5|6|7|1O|8|@]|9|@]|A|$]]|$1|X|3|-4|5|6|7|1P|8|@]|9|@]|A|$]]]|Y|$Z|$5|10|11|12|A|$13|14|15|-4]]|16|$5|10|11|12|A|$13|17|15|-4]]]]

You could set the normalization of the colorcoding such that it is equally spread between the negative absolute value and positive absolute value of the data. Using a colormap with a light color in the middle can help visualizing how far away from zero the values are.

<pre><code>import numpy as np
import matplotlib.pyplot as plt

def show_as_image(sample):
 bitmap = sample.reshape((13, 8))
 maxval = np.max(np.abs([bitmap.min(),bitmap.max()]))
 plt.figure()
 plt.imshow(bitmap, cmap='RdYlGn', interpolation='nearest',
 vmin=-maxval, vmax=maxval)
 plt.colorbar()
 plt.show()

sample=np.random.randn(1,104)
show_as_image(sample)
</code></pre>

<a href="https://i.stack.imgur.com/8WIuS.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/8WIuS.png" alt="enter image description here"></a>

If instead a binary map is required, you may map positive values to e.g. 1 and negative ones to 0.

<pre><code>import numpy as np
import matplotlib.pyplot as plt

def show_as_image(sample):
 bitmap = sample.reshape((13, 8))
 bitmap[bitmap &gt;= 0] = 1
 bitmap[bitmap &lt; 0] = 0
 plt.figure()
 plt.imshow(bitmap, cmap='RdYlGn', interpolation='nearest',
 vmin=-.1, vmax=1.1)
 plt.show()

sample=np.random.randn(1,104)
show_as_image(sample)
</code></pre>

<a href="https://i.stack.imgur.com/JEUKQ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/JEUKQ.png" alt="enter image description here"></a>

In such case the use of a colorbar is probably useless.

I have a bunch of samples with shape (1, 104). All samples are integers( +ve, -ve and 0) which are being used in the <code>imshow</code> function of <code>matplotlib</code>. Below is the function I've created to display them as images.

<pre><code>def show_as_image(sample):
 bitmap = sample.reshape((13, 8))
 plt.figure()
 # this line needs changes.
 plt.imshow(bitmap, cmap='gray', interpolation='nearest')
 plt.colorbar()
 plt.show()
</code></pre>

I need to color code the positive and negative values from the <code>sample</code>. PS: Take 0 as positive.
How do I change my code?

Python matplotlib - Color code +ve and -ve values in plot

我有一堆形状为(1,104)的样本。所有示例都是在matplotlib的imshow函数中使用的整数( +ve、-ve和0)。下面是我创建的将它们显示为图像的函数。def show_as_image(sample):    bitmap = sample.reshape((13, 8))    plt.figure()...

问Python matplotlib -绘图中的颜色代码+ve和-ve值
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