matplotlib & pandas

Python/matplotlab/gallery/scales/aspect_loglog.md

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+# 双对数
+
+![双对数示例](https://matplotlib.org/_images/sphx_glr_aspect_loglog_001.png)
+
+```python
+import matplotlib.pyplot as plt
+
+fig, (ax1, ax2) = plt.subplots(1, 2)
+ax1.set_xscale("log")
+ax1.set_yscale("log")
+ax1.set_xlim(1e1, 1e3)
+ax1.set_ylim(1e2, 1e3)
+ax1.set_aspect(1)
+ax1.set_title("adjustable = box")
+
+ax2.set_xscale("log")
+ax2.set_yscale("log")
+ax2.set_adjustable("datalim")
+ax2.plot([1, 3, 10], [1, 9, 100], "o-")
+ax2.set_xlim(1e-1, 1e2)
+ax2.set_ylim(1e-1, 1e3)
+ax2.set_aspect(1)
+ax2.set_title("adjustable = datalim")
+
+plt.show()
+```
+
+## 下载这个示例
+            
+- [下载python源码: aspect_loglog.py](https://matplotlib.org/_downloads/aspect_loglog.py)
+- [下载Jupyter notebook: aspect_loglog.ipynb](https://matplotlib.org/_downloads/aspect_loglog.ipynb)

Python/matplotlab/gallery/scales/custom_scale.md

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+# 自定义比例尺
+
+通过在墨卡托投影中实现纬度数据的缩放用途来创建自定义比例。
+
+![自定义比例尺示例](https://matplotlib.org/_images/sphx_glr_custom_scale_001.png)
+
+```python
+import numpy as np
+from numpy import ma
+from matplotlib import scale as mscale
+from matplotlib import transforms as mtransforms
+from matplotlib.ticker import Formatter, FixedLocator
+from matplotlib import rcParams
+
+
+# BUG: this example fails with any other setting of axisbelow
+rcParams['axes.axisbelow'] = False
+
+
+class MercatorLatitudeScale(mscale.ScaleBase):
+    """
+    Scales data in range -pi/2 to pi/2 (-90 to 90 degrees) using
+    the system used to scale latitudes in a Mercator projection.
+
+    The scale function:
+      ln(tan(y) + sec(y))
+
+    The inverse scale function:
+      atan(sinh(y))
+
+    Since the Mercator scale tends to infinity at +/- 90 degrees,
+    there is user-defined threshold, above and below which nothing
+    will be plotted.  This defaults to +/- 85 degrees.
+
+    source:
+    http://en.wikipedia.org/wiki/Mercator_projection
+    """
+
+    # The scale class must have a member ``name`` that defines the
+    # string used to select the scale.  For example,
+    # ``gca().set_yscale("mercator")`` would be used to select this
+    # scale.
+    name = 'mercator'
+
+    def __init__(self, axis, *, thresh=np.deg2rad(85), **kwargs):
+        """
+        Any keyword arguments passed to ``set_xscale`` and
+        ``set_yscale`` will be passed along to the scale's
+        constructor.
+
+        thresh: The degree above which to crop the data.
+        """
+        mscale.ScaleBase.__init__(self)
+        if thresh >= np.pi / 2:
+            raise ValueError("thresh must be less than pi/2")
+        self.thresh = thresh
+
+    def get_transform(self):
+        """
+        Override this method to return a new instance that does the
+        actual transformation of the data.
+
+        The MercatorLatitudeTransform class is defined below as a
+        nested class of this one.
+        """
+        return self.MercatorLatitudeTransform(self.thresh)
+
+    def set_default_locators_and_formatters(self, axis):
+        """
+        Override to set up the locators and formatters to use with the
+        scale.  This is only required if the scale requires custom
+        locators and formatters.  Writing custom locators and
+        formatters is rather outside the scope of this example, but
+        there are many helpful examples in ``ticker.py``.
+
+        In our case, the Mercator example uses a fixed locator from
+        -90 to 90 degrees and a custom formatter class to put convert
+        the radians to degrees and put a degree symbol after the
+        value::
+        """
+        class DegreeFormatter(Formatter):
+            def __call__(self, x, pos=None):
+                return "%d\N{DEGREE SIGN}" % np.degrees(x)
+
+        axis.set_major_locator(FixedLocator(
+            np.radians(np.arange(-90, 90, 10))))
+        axis.set_major_formatter(DegreeFormatter())
+        axis.set_minor_formatter(DegreeFormatter())
+
+    def limit_range_for_scale(self, vmin, vmax, minpos):
+        """
+        Override to limit the bounds of the axis to the domain of the
+        transform.  In the case of Mercator, the bounds should be
+        limited to the threshold that was passed in.  Unlike the
+        autoscaling provided by the tick locators, this range limiting
+        will always be adhered to, whether the axis range is set
+        manually, determined automatically or changed through panning
+        and zooming.
+        """
+        return max(vmin, -self.thresh), min(vmax, self.thresh)
+
+    class MercatorLatitudeTransform(mtransforms.Transform):
+        # There are two value members that must be defined.
+        # ``input_dims`` and ``output_dims`` specify number of input
+        # dimensions and output dimensions to the transformation.
+        # These are used by the transformation framework to do some
+        # error checking and prevent incompatible transformations from
+        # being connected together.  When defining transforms for a
+        # scale, which are, by definition, separable and have only one
+        # dimension, these members should always be set to 1.
+        input_dims = 1
+        output_dims = 1
+        is_separable = True
+        has_inverse = True
+
+        def __init__(self, thresh):
+            mtransforms.Transform.__init__(self)
+            self.thresh = thresh
+
+        def transform_non_affine(self, a):
+            """
+            This transform takes an Nx1 ``numpy`` array and returns a
+            transformed copy.  Since the range of the Mercator scale
+            is limited by the user-specified threshold, the input
+            array must be masked to contain only valid values.
+            ``matplotlib`` will handle masked arrays and remove the
+            out-of-range data from the plot.  Importantly, the
+            ``transform`` method *must* return an array that is the
+            same shape as the input array, since these values need to
+            remain synchronized with values in the other dimension.
+            """
+            masked = ma.masked_where((a < -self.thresh) | (a > self.thresh), a)
+            if masked.mask.any():
+                return ma.log(np.abs(ma.tan(masked) + 1.0 / ma.cos(masked)))
+            else:
+                return np.log(np.abs(np.tan(a) + 1.0 / np.cos(a)))
+
+        def inverted(self):
+            """
+            Override this method so matplotlib knows how to get the
+            inverse transform for this transform.
+            """
+            return MercatorLatitudeScale.InvertedMercatorLatitudeTransform(
+                self.thresh)
+
+    class InvertedMercatorLatitudeTransform(mtransforms.Transform):
+        input_dims = 1
+        output_dims = 1
+        is_separable = True
+        has_inverse = True
+
+        def __init__(self, thresh):
+            mtransforms.Transform.__init__(self)
+            self.thresh = thresh
+
+        def transform_non_affine(self, a):
+            return np.arctan(np.sinh(a))
+
+        def inverted(self):
+            return MercatorLatitudeScale.MercatorLatitudeTransform(self.thresh)
+
+# Now that the Scale class has been defined, it must be registered so
+# that ``matplotlib`` can find it.
+mscale.register_scale(MercatorLatitudeScale)
+
+
+if __name__ == '__main__':
+    import matplotlib.pyplot as plt
+
+    t = np.arange(-180.0, 180.0, 0.1)
+    s = np.radians(t)/2.
+
+    plt.plot(t, s, '-', lw=2)
+    plt.gca().set_yscale('mercator')
+
+    plt.xlabel('Longitude')
+    plt.ylabel('Latitude')
+    plt.title('Mercator: Projection of the Oppressor')
+    plt.grid(True)
+
+    plt.show()
+```
+
+## 下载这个示例
+            
+- [下载python源码: custom_scale.py](https://matplotlib.org/_downloads/custom_scale.py)
+- [下载Jupyter notebook: custom_scale.ipynb](https://matplotlib.org/_downloads/custom_scale.ipynb)

Python/matplotlab/gallery/scales/index.md

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+# 刻度、比例尺 
+
+这些示例介绍了如何在Matplotlib中处理不同的比例。

Python/matplotlab/gallery/scales/log_bar.md

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+# 对数条形图
+
+绘制具有对数y轴的条形图。
+
+![对数条形图示例](https://matplotlib.org/_images/sphx_glr_log_bar_001.png)
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+data = ((3, 1000), (10, 3), (100, 30), (500, 800), (50, 1))
+
+dim = len(data[0])
+w = 0.75
+dimw = w / dim
+
+fig, ax = plt.subplots()
+x = np.arange(len(data))
+for i in range(len(data[0])):
+    y = [d[i] for d in data]
+    b = ax.bar(x + i * dimw, y, dimw, bottom=0.001)
+
+ax.set_xticks(x + dimw / 2, map(str, x))
+ax.set_yscale('log')
+
+ax.set_xlabel('x')
+ax.set_ylabel('y')
+
+plt.show()
+```
+
+## 下载这个示例
+            
+- [下载python源码: log_bar.py](https://matplotlib.org/_downloads/log_bar.py)
+- [下载Jupyter notebook: log_bar.ipynb](https://matplotlib.org/_downloads/log_bar.ipynb)

Python/matplotlab/gallery/scales/log_demo.md

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+# 对数演示
+
+具有对数轴的图的示例。
+
+![对数演示](https://matplotlib.org/_images/sphx_glr_log_demo_001.png)
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Data for plotting
+t = np.arange(0.01, 20.0, 0.01)
+
+# Create figure
+fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
+
+# log y axis
+ax1.semilogy(t, np.exp(-t / 5.0))
+ax1.set(title='semilogy')
+ax1.grid()
+
+# log x axis
+ax2.semilogx(t, np.sin(2 * np.pi * t))
+ax2.set(title='semilogx')
+ax2.grid()
+
+# log x and y axis
+ax3.loglog(t, 20 * np.exp(-t / 10.0), basex=2)
+ax3.set(title='loglog base 2 on x')
+ax3.grid()
+
+# With errorbars: clip non-positive values
+# Use new data for plotting
+x = 10.0**np.linspace(0.0, 2.0, 20)
+y = x**2.0
+
+ax4.set_xscale("log", nonposx='clip')
+ax4.set_yscale("log", nonposy='clip')
+ax4.set(title='Errorbars go negative')
+ax4.errorbar(x, y, xerr=0.1 * x, yerr=5.0 + 0.75 * y)
+# ylim must be set after errorbar to allow errorbar to autoscale limits
+ax4.set_ylim(bottom=0.1)
+
+fig.tight_layout()
+plt.show()
+```
+
+## 下载这个示例
+            
+- [下载python源码: log_demo.py](https://matplotlib.org/_downloads/log_demo.py)
+- [下载Jupyter notebook: log_demo.ipynb](https://matplotlib.org/_downloads/log_demo.ipynb)

Python/matplotlab/gallery/scales/log_test.md

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+# 对数轴
+
+这是使用semilogx为x轴分配对数刻度的示例。
+
+![对数轴示例](https://matplotlib.org/_images/sphx_glr_log_test_001.png)
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+fig, ax = plt.subplots()
+
+dt = 0.01
+t = np.arange(dt, 20.0, dt)
+
+ax.semilogx(t, np.exp(-t / 5.0))
+ax.grid()
+
+plt.show()
+```
+
+## 下载这个示例
+            
+- [下载python源码: log_test.py](https://matplotlib.org/_downloads/log_test.py)
+- [下载Jupyter notebook: log_test.ipynb](https://matplotlib.org/_downloads/log_test.ipynb)

Python/matplotlab/gallery/scales/power_norm.md

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+# 探索规范化
+
+多元正态分布的各种归一化。
+
+```python
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import numpy as np
+from numpy.random import multivariate_normal
+
+data = np.vstack([
+    multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
+    multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
+])
+
+gammas = [0.8, 0.5, 0.3]
+
+fig, axes = plt.subplots(nrows=2, ncols=2)
+
+axes[0, 0].set_title('Linear normalization')
+axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)
+
+for ax, gamma in zip(axes.flat[1:], gammas):
+    ax.set_title(r'Power law $(\gamma=%1.1f)$' % gamma)
+    ax.hist2d(data[:, 0], data[:, 1],
+              bins=100, norm=mcolors.PowerNorm(gamma))
+
+fig.tight_layout()
+
+plt.show()
+```
+
+![探索规范化示例](https://matplotlib.org/_images/sphx_glr_power_norm_001.png)
+
+## 参考
+
+此示例中显示了以下函数，方法，类和模块的使用：
+
+```python
+import matplotlib
+matplotlib.colors
+matplotlib.colors.PowerNorm
+matplotlib.axes.Axes.hist2d
+matplotlib.pyplot.hist2d
+```
+
+## 下载这个示例
+            
+- [下载python源码: power_norm.py](https://matplotlib.org/_downloads/power_norm.py)
+- [下载Jupyter notebook: power_norm.ipynb](https://matplotlib.org/_downloads/power_norm.ipynb)

Python/matplotlab/gallery/scales/scales.md

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+# 比例尺
+
+说明应用于轴的比例变换，例如： log，symlog，logit。
+
+![比例尺示例](https://matplotlib.org/_images/sphx_glr_scales_001.png)
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import NullFormatter
+
+# Fixing random state for reproducibility
+np.random.seed(19680801)
+
+# make up some data in the interval ]0, 1[
+y = np.random.normal(loc=0.5, scale=0.4, size=1000)
+y = y[(y > 0) & (y < 1)]
+y.sort()
+x = np.arange(len(y))
+
+# plot with various axes scales
+fig, axs = plt.subplots(2, 2, sharex=True)
+fig.subplots_adjust(left=0.08, right=0.98, wspace=0.3)
+
+# linear
+ax = axs[0, 0]
+ax.plot(x, y)
+ax.set_yscale('linear')
+ax.set_title('linear')
+ax.grid(True)
+
+
+# log
+ax = axs[0, 1]
+ax.plot(x, y)
+ax.set_yscale('log')
+ax.set_title('log')
+ax.grid(True)
+
+
+# symmetric log
+ax = axs[1, 1]
+ax.plot(x, y - y.mean())
+ax.set_yscale('symlog', linthreshy=0.02)
+ax.set_title('symlog')
+ax.grid(True)
+
+# logit
+ax = axs[1, 0]
+ax.plot(x, y)
+ax.set_yscale('logit')
+ax.set_title('logit')
+ax.grid(True)
+ax.yaxis.set_minor_formatter(NullFormatter())
+
+
+plt.show()
+```
+
+## 下载这个示例
+            
+- [下载python源码: scales.py](https://matplotlib.org/_downloads/scales.py)
+- [下载Jupyter notebook: scales.ipynb](https://matplotlib.org/_downloads/scales.ipynb)

Python/matplotlab/gallery/scales/symlog_demo.md

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+# Symlog演示
+
+示例使用symlog（对称对数）轴缩放。
+
+![Symlog演示](https://matplotlib.org/_images/sphx_glr_symlog_demo_001.png)
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+dt = 0.01
+x = np.arange(-50.0, 50.0, dt)
+y = np.arange(0, 100.0, dt)
+
+plt.subplot(311)
+plt.plot(x, y)
+plt.xscale('symlog')
+plt.ylabel('symlogx')
+plt.grid(True)
+plt.gca().xaxis.grid(True, which='minor')  # minor grid on too
+
+plt.subplot(312)
+plt.plot(y, x)
+plt.yscale('symlog')
+plt.ylabel('symlogy')
+
+plt.subplot(313)
+plt.plot(x, np.sin(x / 3.0))
+plt.xscale('symlog')
+plt.yscale('symlog', linthreshy=0.015)
+plt.grid(True)
+plt.ylabel('symlog both')
+
+plt.tight_layout()
+plt.show()
+```
+
+## 下载这个示例
+            
+- [下载python源码: symlog_demo.py](https://matplotlib.org/_downloads/symlog_demo.py)
+- [下载Jupyter notebook: symlog_demo.ipynb](https://matplotlib.org/_downloads/symlog_demo.ipynb)