diff --git a/README.rst b/README.rst
index 8503cddaca..555a91f174 100644
--- a/README.rst
+++ b/README.rst
@@ -78,6 +78,13 @@ Or via conda-forge:
conda install -c conda-forge pymc3
+Plotting is done using `ArviZ `__
+which may be installed separately, or along with PyMC3:
+
+::
+
+ pip install pymc3[plots]
+
The current development branch of PyMC3 can be installed from GitHub, also using ``pip``:
::
diff --git a/docs/source/notebooks/BEST.ipynb b/docs/source/notebooks/BEST.ipynb
index cbf855fde7..20a7467db8 100644
--- a/docs/source/notebooks/BEST.ipynb
+++ b/docs/source/notebooks/BEST.ipynb
@@ -16,7 +16,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Running on PyMC3 v3.3\n"
+ "Running on PyMC3 v3.6\n"
]
}
],
@@ -69,9 +69,9 @@
"outputs": [
{
"data": {
- "image/png": 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AhqHYAcAwFDsAGIZiBwDDUOwAYBiKHQAMQ7EDgGEodgAwDMUOAIah2AHAMBQ7ABjGp2I/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\nYJj/A9HTzJ6+APkqAAAAAElFTkSuQmCC\n",
+ "image/png": "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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -90,7 +90,7 @@
"y2 = np.array(placebo)\n",
"y = pd.DataFrame(dict(value=np.r_[y1, y2], group=np.r_[['drug']*len(drug), ['placebo']*len(placebo)]))\n",
"\n",
- "y.hist('value', by='group');"
+ "y.hist('value', by='group', figsize=(12, 4));"
]
},
{
@@ -128,8 +128,8 @@
"μ_s = y.value.std() * 2\n",
"\n",
"with pm.Model() as model:\n",
- " group1_mean = pm.Normal('group1_mean', μ_m, sigma=μ_s)\n",
- " group2_mean = pm.Normal('group2_mean', μ_m, sigma=μ_s)"
+ " group1_mean = pm.Normal('group1_mean', mu=μ_m, sd=μ_s)\n",
+ " group2_mean = pm.Normal('group2_mean', mu=μ_m, sd=μ_s)"
]
},
{
@@ -173,9 +173,9 @@
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -186,7 +186,7 @@
"with model:\n",
" ν = pm.Exponential('ν_minus_one', 1/29.) + 1\n",
"\n",
- "pm.kdeplot(np.random.exponential(30, size=10000), shade=0.5);"
+ "pm.kdeplot(np.random.exponential(30, size=10000), fill_kwargs={'alpha': 0.5});"
]
},
{
@@ -254,15 +254,16 @@
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
- "Multiprocess sampling (2 chains in 2 jobs)\n",
- "NUTS: [ν_minus_one_log__, group2_std_interval__, group1_std_interval__, group2_mean, group1_mean]\n",
- "100%|██████████| 2500/2500 [00:03<00:00, 727.76it/s]\n"
+ "Multiprocess sampling (4 chains in 4 jobs)\n",
+ "NUTS: [ν_minus_one, group2_std, group1_std, group2_mean, group1_mean]\n",
+ "Sampling 4 chains: 100%|██████████| 10000/10000 [00:04<00:00, 2137.47draws/s]\n",
+ "The acceptance probability does not match the target. It is 0.8794089803204976, but should be close to 0.8. Try to increase the number of tuning steps.\n"
]
}
],
"source": [
"with model:\n",
- " trace = pm.sample(2000, cores=2)"
+ " trace = pm.sample(2000)"
]
},
{
@@ -279,9 +280,9 @@
"outputs": [
{
"data": {
- "image/png": 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ucDVZGena9vaLur5pa8eylxcs0MCBj+jOO+/UnDlz9N13X6hXr156441XtWHD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\nKK9Lir3fMCqIs2fPGgMGDDCee+45Y8mSJbmW33333W6Iqvh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+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -289,7 +290,7 @@
}
],
"source": [
- "pm.plot_posterior(trace, varnames=['group1_mean','group2_mean', 'group1_std', 'group2_std', 'ν_minus_one'],\n",
+ "pm.plot_posterior(trace, var_names=['group1_mean','group2_mean', 'group1_std', 'group2_std', 'ν_minus_one'],\n",
" color='#87ceeb');"
]
},
@@ -309,9 +310,9 @@
"outputs": [
{
"data": {
- "image/png": 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3m1uOr1mzZoFy/KEfv8mW4wG+nzOe5CvRpCUncWb777Ru3TrLchcP7iLi2AHa\nDH0ekznrn2f2lGTWr1/PfffdT9mygVy8eAHDyH6eTUSKB53BEpd18OABFix4nQsXzmO1Wtmw4Udm\nz57Lb7/9go+PL3fffQ9wdVj0Kv+77ONPKQlxXDy4iwZdrw7tXq/zw2x880WsXl50eHIhh4+lZtue\nh7cPjR58NPMm5FYDxrJp8av0+XoxpSvUyXKWDKBcaCPKhc4h3Z6W5f327duzYOAwjv/2PWUq16BZ\nnyGYzRYaPtCX72aMwmSxElKnIeXrNSHy5GHe+vX/oG1f2gydyB9LXrs6TLuHJ3eN/OtesZ0rP6BJ\nz0EAtGzZkoi3FrF2xmga/2PA3+xlEZGi4WE1Z32Gn6kqcUE1adPtYUxmM62feJqXwyI4snE1Ht6+\nvDv04RzzJuSc459++mkupVu59+nZOW//uhw/adIknnj6eQzDIPiW+nnO8VWbt2XD/ElZcjxAnfYP\nsG7WOKyepWjWewhly5YlcdchwlYs4c7Bz3Hw+y+Jj7jIdy+NuRqPrx/tx88BYO+3/+GpgQMxm81U\nrlwFgGHDnqBLl25/p8tFpJCYjDx+BXL5clxhx1Ig/v7exMS412VQw354AgCbzUpamp33Oi51ckSO\n9Xf3WZkAHzys+Tv5mt8H75a0+W/2++2Ov2fgvu0C57ctONjv5jM5QXE9lhU2VziuBAf7FbvcWNzm\nLymfX2fnL3ekPi24ghzPdAZLXE62bzlvYmLTmw/jLiIiIiLiCCqwREo4e4aRp29n/pwn1Z5BbHRC\nYYclIiLXyGuu/pNytYjzqMASKeGsZpPOCEqJ4evridVqcXYYRc5mu3q4N5lM2GxW/P29nRyR5FdB\ncrWr7meLxeyysRdX6tOipQJLRERKjPj4FGeH4BRpaVeH8/7zHqzieC9Gcb1vz5UVx/2cF7pfyPHU\npwVXkNykYdpFREREREQcRAWWiIiIiIiIg6jAEhERERERcRAVWCIiIiIiIg6iAktERERERMRBVGCJ\niIiIiIg4iAosERERERERB1GBJSIiIiIi4iAqsERERERERBxEBZaIiIiIiIiDWJ0dgIiIiIg4lj3D\nIDjYL8/zp9oziI1OKMSIREoOFVgiIiIibsZqNvFyWESe55/YNKgQoxEpWXSJoIiIiIiIiIOowBIR\nEREREXGQPF8i6OvridVqKcxYCsRiMePv7+3sMBzKZru6W0wmEzab1e3a5477rKRxl/3nzp9Fd26b\niIhIcZbnAis+PqUw4ygwf39vYmISnR2GQ6Wl2YGrhVZamt3t2vd391l+btoVx7NnGNhsef+ypTjf\nOO2O+eNPzm6bfk9FRKSk0iCEnCeMAAAgAElEQVQXIpIvunFaREREJHcqsMThygT44GG98e191367\nXZzPcIiIiIiI5IcKLHE4D6tZZzhEREREpERSgSVOl9+HIYqIiIiIFFcqsMTpdE+PiIiIiLgLPQdL\nRERERETEQVRgiYiIiIiIOIgKLBEREREREQfRPVgiIlJi+Pp6YrXm/UHZ7sJmu3q4N5lM2GxW/P29\nC3+jZjM2i6nwtyMOUySfizywWMzFJhZ3oT4tWiqwRESkxIiPT3F2CE6RlmYHrhZaaWl2YmISC32b\nwcF+GsDIxRTF5yIv/P29i00s7kJ9WnAFGelalwiKiIiIiIg4iAosERERERERB1GBJSIiIiIi4iAq\nsERERERERBxEBZaIiIiIiIiDqMASERERERFxEA3TLiIiIlLC2TOMfA1HnWrPIDY6oRAjEnFdKrBE\npFDpoC0iUvxZzSY9t0zEQVRgiUih0kFbREREShIVWHJTZQJ88LDqdj0RERERkZtRgSU35WE16wyE\niIiIiEge6LSEiIiIiIiIg6jAEhERERERcRAVWCIiIiIiIg6ie7CkxDIyMtj0/lyizxzHYrXR+sln\n8a9UjZdeeom1m7Zi8/QGoOEDfal4awt+nDeRlLgrtHxsDOVCGwGwfu4EHp8/i+t/leIunWfDGy/w\n4Oylme+FrViCp18ZaDqMFaMexicwBJPZQro9lYqNWtDsn4OJu3Ser57rT1CNuhgYWKw27pv8DFC5\nqLpFRMSt5Zb7N38wn0tH9mbm/tvHDSPdJzTX3N/6ifH4BIZkWfeNcn/9+3pmyf1hngZGzSa55v6m\n/3ySkNoNi65jRMRhVGBJiXV626+kJsbT7aWFXLlwli0fvUnHCXNJTEzkziETCaxeJ3Pec7u2UC70\nVmq17cyOTxZRLrQRZ8I2UbbaLZQvXx7O530QkD91fP41bF7eGBkZrJs1josHd+FdNoQyFavS5cUF\nAFy5cJbJkyfTeNQs/MpVcljbRURKqtxyf1pyUpbc365pEF98uDrX3H99cZVXf+b+5xqX5d6e/XLN\n/T/Om0iHZ19R7hdxQbpEUEqsKxfOEnxLfQBKl69MfMQFMjLSSUjI/pDb5LhYSpUpi7d/EMlxMWRk\npLN/zWc0erDf347DZDYTVKseVy6czTatdPnKDBo0iD2rPv7b2xERkdxzf1pSYrZ5CzP3m2+S+xs+\n0Fe5X8RF6QyWlFgBVWqyb82n1O/6T+IunCX+UjgpV2JJSEjgwBcfkBofh3dgMLc//hQ+gSGc27mZ\n2POn8Qksx5GfvqXmnR3Z/fUynv8mDnuLBwisUSfL+mPDT7N2+qjM1/GXz9Og2yPZ4rCnpnB+3w5q\nte2cY5z16tUj5oP/c2zjRURKqNxyvz0liZ3X5P5hr710w9yfFB1Bvc49C5z7k5OTb5j7A6vX5shP\nqxzbeBEpEnkusHx9PbFaLYUZS4FYLGb8/b2dHYZD2WxXd4vJZMJms7pd+4qLyk1bc/HwHtZOG0lA\n1VqUqVQNA4M+ffrwU2ogZSpWZdeXHxG24n1aDRjHkQ3fsvnD12nRdwQ7VrxP4+6PEXcpnKlTp/LA\nEyPp8NyrWdZ/7eUecPU6/Gv9MGc8JvPV36nQ9g8SUKUmcZfOZ4szLS0tc76SwJ5hEBzsl+f509IN\nyMgo0LbcMX/8yZ3bJvJ35Jb7Q9s/hH/lGpm5/+2336Zc16G55v7bBz7NxjenFjj37/Oz3TD3Z9jt\nJSr3i7iTPBdY8fEphRlHgfn7exMTk/20vitLS7MDVwuttDS7w9tXJsAHD6uuDgW4rfeQzJ8/H9OL\nUqUD6HhbKNv/92Dlai3uYtOSeZjMZtqOmAxA2Ir3afRAX+IjLuAbVJ5SpUrleGnJzfx5Hf7N7N27\nl7LVa+d7/a7Kajbl+8HWl6MK9jvijvnjT85uW36KZJGillPur9by7sz3qrW4i0OfvknDbrnnfqun\n19/K/RObBt0w10UcP1iicr+IO9ElgiWQh9Wc7z9g3VHUqSPsX7uCNsMmcXbnZgJrhGIymxk2bBhB\nD4/CN6g85/eHEVClZuYyiVGXuXLhLE17PUn4nq1cPLiLpKQkLB4ehRLjlQtn+fDDD2nxzGuFsn4R\nkZImt9y/fu5z3D7w6czcX7v2X8VNTrnfnpJcqLl/3+pP6Tz5jUJZv4gULhVYUmIFVKmFkZHBt1OG\n4OHtl/ktZb9+/Zgw6wWsnqWwennRZtgLmcvsXPkBTXoOAqB8/absW/Mpjz32GHU7P+qwuP68fj8j\nIx2T2cwbc+bwvam8w9YvIlKS5Zb763V6mA3z/8r98//1GotPG0DOuX/tjNE0/scAh8V1fe5vM/wF\nfIOU+0VckQosKbGuvezvWm3atOGB2XVzXOaOJ5/L/NlssdJxwrwcL/PwC6mQ5TkoAE17Dcr8udeC\nL3Jcv19IBfp/uD7Le82aBvF9Ps44iohI7nLL/ZUat6JS41aZrwMDA+H01dybU+7PiSNzv4i4LhVY\nIiJSYhTXAZsKmwZPksJQWJ8jDdLjeOrToqUCS0RESoziOmBTYXPE4EkaIEmuZc8wsNny/mVFqj2D\n2Ojsz5nMibMH6XFH6tOCK8igTSqwRERE5KY0QJJcqyAjvoqUFPoqSkRERERExEF0BktEXFp+H0yc\nn8tURERERPJLBZaIuDRdpiIiIiLFiS4RFBERERERcRAVWCIiIiIiIg6iSwSd7K23XmPfvr2YTCbG\njh1PvXoNMqdlpGVw9svTDPqkP0uWLMt8//jxo0ycOJ7evfvy8MO9nRG2iEuJPnOcH+dOoH7X3tB0\nWJZpv/66kY8+WorNZqNDh048/HBvkpOTmTVrGtHRUaSkpPD4409y551tnRS9yI3d6DiSkpLCq6/O\n4ujuI9wypDYASeGJ9OjRlUqVKgNQq9YtPPXUc1y8eIHZs2eQnm7HYrEydeoMAgN1Sa04xrX3yx4+\nfJgRI0bw+OOP069fvyzzffzxx3zzzTeYTGZuuSWUsWPHExFxmdmzZ5CWlkpGRgajRz9N3br1GDiw\nLz4+vpnLvvjiTIKDQ4q0XSI5UYHlRGFh2zl79gwLF37AiRPHmT17OosXf5Q5/dy6c3iVLwWn/lom\nKSmJ11+fy223tXRCxCKuJy05ic0fzKdCw+bZpmVkZPD663NZsmQ5ZcqU4ZlnxtC2bTv27NlF3br1\nePTRAVy4cJ5x40aqwJJi6WbHkXfeeZM6dUL5efeGzPcyUjNo1649Y8eOz7KuxYvf5cEHe9C+fUe+\n+OIzPv30Y0aMGFtkbRH39uf9smnJSax/dSqlazfl+zPxnL3mHtrUxAS+fmcRD7/5KZOal6dfv8fY\nu3cPGzf+yF13taN794fZs2cXixa9w/z5bwOwYMEiZzVJJFcqsJxo+/attG3bDoAaNWoSFxdHQkJ8\n5vSKHSuSfCUlS4Fls9mYN+9Nli+/egDVgx9Fbsxis9Fx4mvs+Xp5tmnR0dH4+voSEBAAwG23tWDb\ntv/StesDmfNcvHiRkBB9IyrFU27HkT+/1R86dCSxsbEs/vy9zGXSUzNyXNf48RPx8PAAwN8/gMOH\nDxZu8FIi3Sgnm61WzFYbaclJ2O12kpOTKV26NGXK+HPlSiwAcXFx+Pv7A5CYqAfnSvGkAsuJIiMj\nCQ2tm/m6bNmyREZGZr62eGZ/QrrVasVq/Wu35ffBj6BR1KRkMVusmC1//c5ce5mKYfiSkpJMQkIk\nlSpVYu/enbRs2TJzep8+fTh//gIvvzzfKbGL3Exux5E/Cyxvbx9iY2OzLJORmsHuvTsZP34MyclJ\nDBo0lGbNmlOqVCkA0tPT+fLLFTz++JNF1xApMa7PydeyenjSpOdAPh/Tix99vbnnno5UrVqN3r37\nMnjwAL77bjUJCQm88877AMTGxjJ9+mQuXAinadPmDB48HJPJVJTNEcmRCiynMrK+MgwlBpFCdv2w\n7rcOep7HxjyLzdsX36BybAxPIOp/05tMWMCLXpcZP/4ZPvzwP/r9lGIo/8cRr3JeDGzxJG3a3M3p\n06cYN24En376FTabjfT0dF56aSrNmjWneXNdii5FKzUxgd1f/ZuHX/+ESa2r0rdvP44cOczvv//C\nvfd2YMCAQfz++6/8619vMnv2XIYOHUmnTvfh6enFxIlP8/PPP9GuXXtnN0NEowg6U1BQcJYzVhER\nEdSoVRVPTxuenjbMZhMeHlasVjPBwX5Z/vn4eOLr6+XE6EXcQ/n6Tek6/V06TpiLzdsH3+AKRBw/\nSHzERQDq1atHeno6MTHRTo5UJLucjiOBgYE3XMYr2Is2be4GoGrVagQGBnL58iUAZs+eTpUqVXni\niSGFF7RILmLPncQvpBJepf3x8PCgceOmHDp0gD17dtOq1R0AtGjRioMH9wPQo0dPfHx8sVqt3HFH\nW44dO+rM8EUyqcByopYtb2fjxh8BOHz4IEFBQQSU8WN/dErmvyOxqVxIsvNyWESWf7+dT+T7M/E3\n2YKI3Mz3c8aTfCWatOQkzmz/nYoNm3PxwE72rf4PcPUP1sTERMqU8XdypCLZ5XQc8fb2ueEy0WFR\nrFjxCQCRkRFERUURHBzC99+vxWazMWjQ0EKPWyQnvsEViDl3EntqCoZhcPDgfqpUqUrlypXZv38v\nAAcOXH0vJiaGZ54Zg91uB2Dnzh3UqFHLmeGLZNIlgk7UqFFjQkPrMWzYE5hMJp5+egIrV64k/fAV\nLHVKk/L1aYy4NGKjYe30UdRp/yBlKlZl67IFxF8+j8lqpf/+37llyHQ8fUs7uzkixVLE8YPZfmeM\n0Fb4BVegWsu7qdP+AdbNGofVsxTNeg/Bq7Q/oR178Pt7c1jz4nD+a03n6acnYDbr+ygpfnI6jqxZ\nswofH1/uvvseJk+ewKVLF0mNTOH4h8cIbhlM6bql2fLLJjZu/JHU1FSeeWYiNpuNlStXkJqawqhR\nV89eVa9ek2eemejkFoq7uT4nn9qykSrN22Tm5IYP9OW7GaPYV9qLunUb0LhxUypVqsLLL8/gp59+\nAGDcuGfx9/enWbPmDB06EA8PG7Vrh9Ku3b1Obp3IVSqwnGz48NFZXt9xx23MiPoYAM+HqgLQ57aF\nWebp8uKCzJ8nNg3K9yAXIiVJUM26N/ydqd6yHdVbtsuyjNXDk7vHTMuc//LluKIIVaRArj+O1K5d\nJ/PnmTNfAWDYD08AYLNZSUuzM2/eW9lGof3iixVFEK2UdNfn5OvV7dCduh2680zjQKzmq/cTBgf7\n8dFHH2Sbd+zYkYwdOxKAVHsGsdEJhRO0SD6pwCpkGkZdRESKo/yOQqsRaKUoXT8g0c080zgwcwTY\nvFBBJoVJBVYh0wFMxLVdO6x7XuigLSJS9PJbkOnvLSlMKrBERG5AB20RERHJDxVYbuDY1t/4es5z\nXD55JE/zP5/P9Wt+ze/K8wdXr81Dz79KrRZt8rnmgtEZL3EX1x9bnP27rPndY/6izskizqACyw18\nOWs8kaePOzsMkWLp8skjfDlrPM98taVItqczXuIudGyRwlDUOVnEGUp8gZXfQSjSMgxs/xvVprCU\n9irPleQL//u5QqFuS0ScS2e8xFFudDyrEVSN8LhwAGoGVc/XZ07EHSn3SmFyuwKrIKP25ffb5sL+\ndvq2qn3Zemr5/35+5Kbz93jhNb55eQKXThzO97ZE3F1IjTo8OPEVZ4eRK53xktw48niW7tWD0xev\nHlfKBXfn5bCIm36WdGyRwlBccrJGKZTCVOQFVmGcMbr+A+/qf6yU8wulW8OX8jx/rRZteOqL3/M8\nf0GKRM2v+d1lfleXn29dg4P98n3WXX8U5F1RXAHhqONZfo8rkP3YUtx+lzW/e89f3BR2QZbf/FCQ\n+YtTPAU51uQ35zrzeGYyDMNwypYdZOPGjbRr187ZYRQKd22b2uV63LVt7toucO+2ScHpc+F46lPH\nU586nvq0aLn8E3B//vlnZ4dQaNy1bWqX63HXtrlru8C92yYFp8+F46lPHU996njq06Ll8gWWiIiI\niIhIcWGZNm3aNGcH8XdVr17d2SEUGndtm9rlety1be7aLnDvtknB6XPheOpTx1OfOp76tOi4/D1Y\nIiIiIiIixYUuERQREREREXEQFVgiIiIiIiIO4nIPGk5LS2PixImEh4djsViYM2cOVapUyTJPmzZt\nqFGjRubrDz/8EIvFUtSh5tns2bPZtWsXJpOJSZMmceutt2ZO27RpE/Pnz8disXDXXXcxcuRIJ0aa\nPzdqV/fu3fHz++t5DPPmzaNcuXLOCLNADh8+zIgRI3j88cfp169flmmuvM/gxm1z5f326quvsn37\ndux2O0OHDqVTp06Z01x5n92oXa68v8TxbpSTpWBulC+lYG6U0yT/kpKSmDhxIpGRkaSkpDBixAju\nueceZ4fl/gwXs3LlSmPatGmGYRjGxo0bjbFjx2aZnpGRYfTo0cMZoRXIli1bjCFDhhiGYRhHjhwx\nevbsmWV6ly5djPDwcCM9Pd3o3bu3ceTIEWeEmW83a9dDDz3kjLAcIiEhwejXr58xefJkY9myZdmm\nu+o+M4ybt81V99sff/xhPPnkk4ZhGEZUVJRx9913Z5nuqvvsZu1y1f0ljneznCz5d7N8Kfl3s5wm\n+bd69Wpj0aJFhmEYxtmzZ41OnTo5OaKSweUuEfzjjz/o2LEjcPVM1fbt27NMT0xMJD093RmhFcgf\nf/xBhw4dALjlllu4cuUK8fHxAJw5c4YyZcpQoUIFzGYzd999N3/88Yczw82zG7ULICHBOU/WdgQP\nDw8WL15MSEhItmmuvM/gxm0D191vLVq04M033wSgTJkyJCUlZeYJV95nN2oXuO7+Ese7WU6W/LtZ\nvpT8u1lOk/zr2rUrgwcPBuD8+fO6iqGIuFyBFRERQdmyZQGwWCyYzWZSU1MzpycmJhIZGcmYMWPo\n06cP//73v50Vap5EREQQEBCQ+TowMJDLly8DcPny5cy2AgQFBWVOK+5u1C6AmJgYxo8fT58+fXj9\n9dcxXGgwS6vVipeXV47TXHmfwY3bBq673ywWC97e3gCsWLGCu+66K/OyYVfeZzdqF7ju/hLHu1lO\nlvy7Wb6U/LtZTpOC69OnD8888wyTJk1ydiglQrG+B2vFihWsWLEiy3u7du3K8towDEwmU+brUqVK\nMXbsWB566CHS0tLo168fzZo1o2HDhkUSc35d/wfPte3J6Y+ha9tanN2oXQBPPfUUDz74IJ6enowY\nMYLvv/+ezp07F3WYDufK+ywvXH2/rV+/ns8//5ylS5dmvucO+yyndoHr7y9xnJvlZJHiJLecJgX3\nySefcODAAZ599lm++eYb/f4XsmJ9BqtXr1589tlnWf716NEj81u3tLQ0DMPAZrNlLuPr60uvXr3w\n8PDAx8eH1q1bc+jQIWc14abKlStHRERE5utLly4RFBSU47SLFy8SHBxc5DEWxI3aBdC3b198fX2x\n2Wy0a9euWO+j/HDlfZYXrrzffv31V9577z0WL16cZeAHV99nubULXHt/iWPdLCeLFBc3ymmSf3v3\n7uX8+fMA1KtXj/T0dKKiopwclfsr1gVWTu68806+++47ADZs2ECrVq2yTD906BATJkzAMAzsdjs7\nduygdu3azgg1T+68807WrVsHwP79+wkJCcHX1xeAypUrEx8fz9mzZ7Hb7WzYsIE777zTmeHm2Y3a\nFRUVxeDBg0lLSwNg69atxXof5Ycr77ObceX9FhcXx6uvvsrChQvx9/fPMs2V99mN2uXK+0sc70Y5\nWaS4uFFOk4LZtm1b5pnAiIgIEhMTs1wuLIWjWF8imJOuXbuyadMmHnnkETw8PHj55ZcBWLRoES1a\ntKBp06b4+/vTq1cvzGYz99xzT7EeirZZs2Y0aNCAPn36YDKZePHFF1m5ciV+fn507NiRadOmMX78\neOBq268dfr44u1m7WrVqRe/evfHw8KB+/fouddnS3r17eeWVVzh37hxWq5V169Zx7733UrlyZZfe\nZ3DztrnqfluzZg3R0dGMGzcu871WrVoRGhrq0vvsZu1y1f0ljpdTTpa/J6d8+fbbb6sw+Btyymmv\nvPIKFStWdGJUrq1Pnz688MIL9O3bl+TkZKZOnYrZ7HLnV1yOydBdzyIiIiIiIg6hElZERERERMRB\nVGCJiIiIiIg4iAosERERERERB1GBJSIiIiIi4iAqsERERERERBxEBZaIiIiIiIiDqMASERERERFx\nEBVYIiIiIiIiDqICS0RERERExEFUYImIiIiIiDiICiwREREREREHUYElIiIiIiLiICqwpETbsmUL\nnTp1Yu3ataxatYrOnTuzbdu2fK1j3bp1eZpv1qxZnDlzpiBhioiIiIiLUIElJdrWrVvp27cvXbp0\nYdOmTTz77LM0b948z8ufPXuW1atX52neF154gSpVqhQ0VBERERFxAVZnByBSFNLT05kyZQpnzpzB\nbrczZswYypYty8qVK7FarYSEhPDLL7+wd+9eSpcuTUxMDEuXLsVqtdKwYUMmTpxIWloaEydO5Ny5\nc3h6evLqq68yY8YMdu/ezYIFCxg1alTm9r766iuWL1+OzWajbt26vPjii/Tv358pU6bw3XffsXXr\nVgAOHz7MlClTaNeuHZMmTSI2Npb09HQmT55M3bp1ndVdIiIiIlJAKrCkRFi1ahXBwcHMnj2bqKgo\nBgwYwKpVq+jRowcBAQF07dqVX375hc6dO9OgQQP69evHp59+ioeHB2PHjmX79u0cP36coKAgXnvt\nNVavXs2PP/7IoEGD+Pjjj7MUVwBLlixh0aJFVKhQgS+++ILk5OTMaWPGjAFg//79zJgxg06dOrF4\n8WLatm1Lr169OHr0KLNmzeKDDz4o0j4SERERkb9PBZaUCGFhYWzfvp0dO3YAkJKSQmpqao7zHj16\nlPDwcAYNGgRAXFwc4eHh7Nu3j9atWwNw//33A1fv4cpJt27dGDlyJA8++CDdunXDy8sry/SkpCQm\nT57Ma6+9hoeHB2FhYURFRfHNN99kThcRERER16MCS0oEm83GsGHD6NatW57mbdiwIUuWLMny/s6d\nO8nIyMjT9oYOHcoDDzzAunXrGDBgAMuXL88yfdasWfTt25caNWpkbnPKlCk0bdo0jy0SERERkeJI\ng1xIidC4cWPWr18PQGRkJPPnz8913ho1anDs2DEiIyMBeOutt7h48SKNGjVi8+bNAGzYsIH33nsP\ns9mc7UxYRkYGr7/+OsHBwQwcOJAmTZoQHh6eOX3dunXEx8fTs2fPHOM7evSoLg8UERERcVE6gyUl\nQpcuXdi8eTN9+vQhPT092z1T1ypVqhSTJk1i8ODBeHh4UL9+fUJCQujatSubNm2iX79+WCwWXn31\nVWw2GwcPHmT27NlMmjQJALPZjI+PD71798bPz48qVapQr169zPXPnz8fHx8f+vfvD0Dnzp3p168f\nzz//PH379iUjI4MXXnihcDtERERERAqFyTAMw9lBiIiIiIiIuANdIigiIiIiIuIgKrBEREREREQc\nRAWWiIiIiIiIg6jAEhERERERcZA8jyJ4+XJcYcbxt/j6ehIfn+LsMPJFMRcNV4vZ1eIFxVxUXC3m\n4GA/Z4cgIiLiFG5xBstqtTg7hHxTzEXD1WJ2tXhBMRcVV4xZRESkJHKLAktERERERKQ4UIElIiIi\nIiLiICqwREREREREHEQFloiIiIiIiIPkeRRBkZKiTIAPHta8f/eQas8gNjqhECMSEREREVehAkvk\nOh5WMy+HReR5/olNgwoxGhERERFxJbpEUERERERExEFUYImIiIiIiDhIni8R9PX1LLYPurRYzPj7\nezs7jHxRzEWjqGJ21DbUx0VDMYuIiEhhyXOBFR+fUphx/C3+/t7ExCQ6O4x8UcxFoyAxBwf75Xs7\njuqXktLHzqaYC19Bfo9ERETcgS4RFBERERERcRAVWCIiIiIiIg6iAktERERERMRBVGCJiIiIiIg4\niAosERERERERB1GBJSIiIiIi4iAqsERERERERBwkz8/BEinpUhPj+fntaaQmJmDzKsXdo6fh6Vs6\nc3psbAx9+z5MjRq1APD3D2DmzFeIjIxg1qzppKQkExAQwKRJ0/D29mbgwL74+PhmLv/iizPx969e\n1M0SEREREQdSgSWSR/vWfEb5+k1p9MCjHPh+Jbu/Xk6LR0dkTk9KSuLWW5swZ85rWZZbtuxD2ra9\nmx49evLdd6v5/PNPeOyxJwBYsGBRkbZBRERERAqXCixxOWvWrGLnzh3ExMRw4sRxhgwZzvr16zh5\n8gRTp87k0KED/PDDWkwmM506daR7995cunSRl16aCoDdbmfy5OlUqlSZ3r2707ZtO/bs2YWvrx9z\n577Bu+++y9p1P2fZZutB4zm/dxtthk0CoGrztvw4d8LV9WUYBAf7ER19Hg8PK8HBflmWvXQpnEce\n6UVwsB9du3ZkzNixPPbYEyQmJhZBb4mIiIhIUVKBJS7pzJnTvPPO+6xa9RXLl3/I0qUfs3btKpYt\nW0pCQgLvvLMEgFGjBnP77XcTHR3JwIGDadasOd9++zUrV65g9OinCA8/x3333c+oUeMYMuRxjh07\nwvDhw4m9vVe2bSbFROFV2h+AUv5lSYyJAMBqNvFyWASXjoSz7cAROvQdREpcLPXu60nNOzoQVaYK\nMz9ZQ5OHy3P0l7VER0UBEBsby/Tpk7lwIZymTZszePDwIuo9ERERESksKrDEJdWtWx+TyURgYBC1\natXGYrEQEBDIsWNHsdvtjB49FIDExAQuXAinQoWKvPHGPJYsWUhc3BVCQ+sB4OPjwy231AYgJCSE\n+Pj4XLdpYFzzwsCEKct0n8ByNP7HQGre0YHkuFi+nTKE8vWacGv3/vyxZB5rpo+kStM7MIyr6xk6\ndCSdOt2Hp6cXEyc+zc8//0T37g84sptEREREpIipwBKXZLFYcvz5ypVY2rfvxHPPvQCAv783MTGJ\nzJ49nVatbqd7955s2I+1Th0AAB4JSURBVLCeTZt+y7YsgGEYuV4i6BMQTFJMJB7eviRGRVAqIDDL\nPD5lg6nVphMApcoEEFSzLrHhp6jQ4DbajZkOQGz4KbxO7wagR4+emcvecUdbjh07+rf6RERERESc\nTwWWuJXQ0Hrs2LGd5ORkPD09mTNnNk88MYyYmBgqVaqMYRj89tvPpKdn5LqO3C4RrHhrC05s3kCT\nfzzOyf9upFLj27NMP7drC+f37aB53+GkJScRdfIIpStU5dCP32BkpFO3Yw+ObFxDj3vvJSYmhpkz\np/Lyy/OxWq3s3LmDdu3aO7w/RERERKRoqcASt/L/7d15fFTlvcfx7yyZ7GRPAFkEBMIuyGrCJiKi\ngtwKwkWsgCsBihi0kUWxFUQKqAVbt4pKF3tBqMVCARW8tQgFjChLAC+yJCQhG2SDJJOc+0dkNLIk\ngZnMTPJ5v16+XnPmnOec7xyeifnlPOc5MTGNNWjQEE2d+rDM5spJLnx9/XT33T/Tyy8vUUxME40e\nPVaLFy/Qf/6zo1b77jh8jP53xa+04dkpsgUGa8C0ykkzFixYoOKeI9S4Uw99+78b9dG8R2VUlKvr\nqPsVGB6lFj37a+uy2Tr6+WaFNGulcePGKTe3WD169NSjj06Szeajtm3ba9CgW1xxSgAAAFCHTMaF\nG0KqkZVV4OosV+3CMDBvQua6cTWZo6KCtSg5u8bbJ3WPrPX2l/s+NZRz7G5kdr2fzqYJAEBDYXZ3\nAAAAAACoLyiwAAAAAMBJKLAAAAAAwEmY5KKOHcpN0R8Pvis/X6tGt75P7cNj3R0JAAAAgJNQYNWx\nPx9cpaziTPmUWfWXlD9q/s3PuzsS6pi9wrjiBAA/XVdqr9DZvCJXxwIAAIATUGDVsczidMfrjKJT\nbkzScISEBcpm9ZzRsFazqVazDs7qFlGrGdkoyAAAANyHAgv1ns1qrvU06p6ktgWZp+UHAABoSDzn\nz/oAAAAA4OUosAAAAADASSiwAAAAAMBJuAcLXsfTJq0AAAAALqDAgtfx9kkrAAAAUH9xGQAAAAAA\nnKTGV7CCgnxltVpcmeWqWSxmhYYGuDtGjfj4VJ5yk8kkHx+r1+SWvOs8N3R1+e/kjf2CzAAAwFVq\nXGAVFpa4Msc1CQ0N0Jkzxe6OUSNlZXZJlYVWWZnda3JLnnOea/PQ3YaqLv+dPKVf1AaZXY/vKQCg\noWKIIAAAAAA4CQUWAAAAADgJBRYAAAAAOAnTtMPjHD36rZKSEjV27Hjdc8/YKuuysk7riScSdKKg\nTJJUcPqUbvrvxxQU1Vj/eW+5fIMb6dZZL8pstaogM027/vQ7Ja16vcbHzj1+RONenKa0IrvCWrTR\nzQ89WaN2xWdy9PnvF8hecl5+jcLUP2GOfPwCdHDTB/q/zzfJZLYosnV79Xng8SrtTh/ep11/elVm\ni0UWH5sGTJ0nn4AgfbIkSSUF+er9819I3QdLkj7+zS/Vb3KiAiOia/x5AAAAULe4ggW3CwkLVFRU\nsKKighUYaNGKFcsUHx+noCA/x/sX/uvYsY1WrVql4c+u0LC5LyswMkYtesbr4KYPdEviQkW37aSM\ng19Jkr78nzd107hHapQhbe9OSdLOd1/R7NmzdeevXlNJYb5Sk7+4aNvz+XnKPppS5b2v/7ZKLW7q\nrzvm/04tevbXgY2rVVpcpH3r/6w75v9Odz73e51JPabTR/ZVabf/H+9rQMJcDX9mhaLadtahT/6u\njP1fKqZ9Vw2e+Wsd2rJOknQyebvCW95AcQUAAODhuIIFt/vxg4Mryu1qP22Rvvnwjzp+slCpl3ig\n8IUHB3/72QZd33ugfPwCVFJwVv6h4fIPjdT5gjM6fWSffINDFdK05WWPW1FRrmM7turgP9coonWs\nGnfqocLT6eratas2JGerRc94ndq3W82695MkFZxO176P/qy849+q531Tq+wrP+OkbhhwuyTpum59\ntO3leep05ziZrT4qO39OPn7+speWyDewUZV2g2c+L0kyDEPFuVmKie2q8wVn5R8SroDvP0t5ebkO\nbPgf3ZL4wlWeYQAAANQVCix4FLPFKrOlZt3y8KfrddvslyVJgRHRKshIU376CbXoNVBfr3tPN46e\nrM9fe0ELWoRLt02WxerzQ9utHyll81pd162Phsx6QX6NwlScmyVb0A9TS/uHROhcXo4KszP05ftv\nqDgvW51HjFe/yYkXZQlr3kapydsV2TpWaXt36Fx+nqw2X904epLW/GKMrL5+atXvVoU0bXFR29Sv\ndmjnOy8r5LqWahM/TJmHvlbaVzt0Nv2EAiNitGbNGrWOG6qvP1ylc3nZ6jBstCJatavtqQUAAEAd\nYIggvNLpw/sU0rSlbAGBkqROd47T9j/8RiVFBSrOy1KTTj106OMP1e2/fq527drp2I6tVdrvW/9n\nxQ4dpR73Piy/RmGSJOOioxiSSco8uFeFWemKnzJbzW7se8k8XUfdrzNpx7Xhuak6dyZXMgyVFhfp\n67+9p3teel+jf7taWd/uV+7xIxe1bXZjX/3spb8opGlLff3hKsW076rivGzteOcltR8yUh9//LEa\nNW4mk8mkvpOeUPLqt6719AEAAMBFKLDglU5++W817dLLsRzWvLWGz1uumx98Uke2/UMdho1WYdYp\nBUY1VtOmTVWYlV6l/e3PLFd+Rpo+mveIDm/9SBV2u/wbhamkIN+xTVFulvxDI9U6/jZ1GTlB//r9\nAn3+2kKdPXX8ojy+gcEa9IvndMezr6pFz3gFRTXW2bRjCo6+Tn6NQmWx+igmtquyjx6q0u74fz6T\nJJlMJl3fZ5AyD30tk9ms/glzNXzecp388t968MEHVZidoaDIxrL6+qnsnPc8bBYAAKChocCCV8r+\nv4MKa3nDRe8f3PyB2g+5W2arVX4h4SrKzlR6eroCwiKrbBcQGqGe46do2JxXVFJwVhvmT5HZalVI\n0xbavXu3pMrip9mNfWQymdT8pjgNn7dcbQeP0O6/vKbDn66vsr9Dn/xdKd9PSHFk2wY17xGnoKgm\nOpN2TPbSEhmGoZyjKWrUuFmVdslr3lbOscOSpKxv9yukyQ9DCItzs5Sfkaq+ffvKPyRcRTmZspec\nl8Vmu/YTCAAAAJfgHix4lOyjKdq1aoUKs9Jlslp1fOc23ZK4UCd2/0u2gCC17D1QUuW06P7fD+27\noKSoQJkpe9Xpjsqp3TsMu0fbXnlWJyOD1eaR5y55PFtAoLqMvE8d77hXktTngRlatmyZThaUKuqG\njlWukklSTPsuimn/gsrtZVXeb9Gzv7Yum62jn29WSLNW6jHuEZnNFnUeMV7//NU0mSxWRbfrrMYd\nbtTBgweVvPpDdR/zkOIfTdIXf1haOU27zVcDpj7j2OdXa1fqxtEPSpIad+yu/Rv+qo2/mq5uP3vg\nGs4wAAAAXMlkGMbFt55cQlZWgauzXLXQ0ACdOeMdw6Ye2zJZkuTjY1VZmV2vDX3bzYlqzlXnOSoq\n2DGLYE0kdY9k+2q2r8vvqzd9/y4gs+tFRQVXvxEAAPUQQwQBAAAAwEkosAAAAADASbgHC6hn7BVG\nrYZnldordDavyIWJAAAAGg4KLKCesZpNtb5nCwAAAM7BEEEAAAAAcBIKLAAAAABwEgosAAAAAHAS\nCiwAAAAAcBIKLAAAAABwEgosAAAAAHASCiwAAAAAcBIKLAAAAABwEgosAAAAAHASq7sDoP4JCQuU\nzUrtDgAAgIaHAgtOZ7OatSg5u8bbJ3WPdGEaAAAAoO5wmQEAAAAAnIQCCwAAAACcpMZDBIOCfGW1\nWlyZ5apZLGaFhga4O0aN+PhUnnKTySQfH6vX5Ja86zyjdq7l39Ub+wWZAQCAq9S4wCosLHFljmsS\nGhqgM2eK3R2jRsrK7JIqC62yMrvX5JZqfp6jooLrIA2c6Vr6oTd9/y4gs+vxcwAA0FAxRBAAAAAA\nnIRZBIEGzl5h1OpqQ6m9QmfzilyYCAAAwHtRYAENnNVsYlp9AAAAJ2GIIAAAAAA4CVewUK2QsEDZ\nrJW1ODeuAwAAAJdHgYVq2axmhpABAAAANcAQQQAAAABwEgosAAAAAHASCiwAAAAAcBIKLAAAAABw\nEgosAAAAAHASZhEEUCv2CuOi6fqvNH1/qb1CZ/OKXB0LAADAI1BgAagVq9nEtP0AAACXwRBBAAAA\nAHASCiwAAAAAcBKGCDZAIWGBslmprQEAAABno8BqgGxWM/fQAAAAAC7AZQwAAAAAcBIKLAAAAABw\nEgosAAAAAHASCiwAAAAAcBIKLAAAAABwEgosAAAAAHASCiwAAAAAcBIKLAAAAABwEh40jHrHqKjQ\n9rd+o7yTR2Wx+mjM0gWSQrRj5TKdPrJPPr4BkqTOI8aradde+mRJkkoK8tX7579QTPsukqSPf/NL\n9ZucqMCI6Cr7Ljidrq0vz9HIhW873lu+fLkOFPqo4+2jtXraPQqMiJbJbFG5vVRNu/RSj3sfVsHp\ndP3tqfsV2SpW+4KsSjsvdb/3IUW37Vxn58Vd7BWGoqKCa7x9qb1CZ/OKXJgIAADAdSiwUO+c2P0v\nlRYX6q5fv678jFQtXrxYrR5boLLz5xT3SJIirm/n2DZt707FtO+qNv2H6cv331BM+y767LPPFN7y\nhouKq5oa+vRS+fgFyKio0KYFjyszZa8CwqMV0rSFhj+7QkndIzV741f6ZEmSbn3yRQXHXOesj+6R\nrGaTFiVn13j7pO6RLkwDAADgWgwRRL2Tn5GqqBs6SpIaNW6mU6dOqaKiXGXnii/a9nzBWfmHhCsg\nNFLnC86ooqJc7777rrqMnHDNOUxmsyLbdFB+RupF6xo1bqbOI8brm/V/uubjAAAAwHPU+ApWUJCv\nrFaLK7NcNYvFrNDQAHfHqBEfn8pTbjKZ5ONj9Zrc3iSseWvt3/BXdbzjXhVkpOrkyZPqkX9W9pJz\n+uqDlSotLFBARJT6TpypwIhopX21Q2fTTygwIkZHPv1Id915p97/cJXO5WWrw7DRimjVrsr+z546\noY3PTXMsW/JPq9nQey/KYS8tUfr+L9Wm/7BL5oy4vq2OfLreuR++nnD198KbfmZc4I2ZAQBoiGpc\nYBUWlrgyxzUJDQ3QmTMXX53wRGVldkmVhVZZmd0tuWtzP4w3ata9nzIPf6ON86cqrEUbtW7dWoYM\ntR9yt0KbtVJI0xbau+5dJa9+S30eeFxHtn6kHe+8pF7jE/Tl6rfUcvB0mXYfVt9JT2jbK8/o1qcW\nV9n/haF+FwR+/hftKvxh/ZYXEmUyV/4xov2QkQpr3loFp9Mvyllhtzu2Q1Wu/l5408+MC7wtc33/\nOQMAwOVwDxbqpZvGPuJ4/fGT4+TfKEwtew90vNey1wBt/8MSmcxm9U+YK0lKXv2WuowYr1OnTiko\nsrGsvn6XHFZYnQv3YFUn+2iKwq9vW+v9AwAAwHNxD5aXCwkLVFRUcK3+q+9yjx/R568tlCSlfrVD\nHTt2lMls1se/eUqF2RmSpPQDyQpr3trRpjg3S/kZqWrS6SZFRkaqKCdT9pLzsthsLsmYn5Gq/f/4\nqzrdOdYl+wcAAIB7cAXLy9ms5lrN0CbV/1nawpq3kVFRoY/mPSJbQLBW/36Z3jxhqMNt92jrsjmy\n+vrL6uen+MfmONp8tXalbhz9oCSpd+/eyv7tG9r4q+nq9rMHnJbrwr1be/1NSjtXofgpcxQU2dhp\n+wcAAID7UWCh3vnxsD9JioiIkE5k67pufXRdtz6XbHPzQ085XlutVg395ZJLbhcc3aTKM7Akafr0\n6Y4id8yKDy7b7v53PpZUWeDWtigGAACAd2CIIAAAAAA4CQUWAAAAADgJBRYAAAAAOAn3YAHwKPYK\no1azXZbaK3Q2r8iFiQAAAGqOAguAR7GaTbWaBKS+z4oJAAC8C0MEAQAAAMBJKLAAAAAAwEkYIuhh\nQsICZbNS9wIAAADeiALLw9isZu4/AQAAALwUBdZV+O1vl2r//n0ymUyaMSNRHTp0cqzbtWun3njj\nVZnNFvXrF6eJEx9yrCspOa9Dr6QoemC0ontFS5LWrHlfy5e/pI0btyogIKDOPwvg7ewVhvLy0pWQ\nkKCJEydqwoQJVdaXlJRo3rx5+vbbb7V27VqV2iv0wZo1WrdunWObQ4cOasuWf+mpp2aqoCBfFotF\nkjRt2kzFxnao088DAAC8GwVWLSUn71Fq6km9/vpKfffdUS1c+JzefPNdx/pXXlmipUuXKyoqWgkJ\nD2ngwFvUqlVrSdI77/xB1gCLY9u8vXnKCc5RZGRUnX8OoL4oPX9OD//yGTVq212bTxYq9SdXgHes\nXKbg6BbKOJeiRcnZSuoeqXvuuUdDhgyXVPmd/vTTjyVJ584Va/HilxUcXPNp4gEAAH6Mm31qac+e\nXerff5AkqVWr1iooKFBRUaEkKS0tVcHBjRQT01hms1n9+sVpz57/SJKOHz+mY8e+U1DbH35xaxTb\nSI8+OlUmk6nOPwdQX9hsNg1NWqqAsEsPl71p3KNq0WvgZdu/885bmjjxQUlScXGxSzICAICGgwKr\nlnJychQaGupYDg8PV05OjiQpNzdHoaFhjnURERGOdStWvKTp02dW2ZfF1yIA18Zqtcpq873seh//\nwMuuO3hwv6KjYxQRUVmcnTtXrGXLXlRCwkNasmSRSkpKnJ4XAADUbxRYtWZUXTIMxxUow/jpOsnP\n36Z///sT9e7dU926xcpqschqtchsNsnX10dRUcGyWMyKjAxSVBTDkoC6tH793zR8+F2O5fvvn6Sp\nUx/Xq6++qfLycq1du9qN6QAAgDfiHqxaioyMclyVkqTs7GxFRERIkqKiopWb+8O6rKzTuu66GP1+\n3SYVnj6lP/9ji7IysmSymJRuNslyfZAWJWfrbEm5ln2dIx+/c8wKCNSh5OQ9mjnzKcfyj4utAQMG\n6pNPtrgjFgAA8GJcwaql3r37atu2TyRJhw+nKDIyUgEBlUOQmjRpqqKiIqWnn5Ldbtf27Z8rLi5O\ngx//tUYs/IPuev5NWbuGyXpztCzXB7nzYwANXnZ2lvz9A+Tj4yNJKi8v14wZUxz3VCYn71Hr1m3c\nGREAAHghrmDVUpcu3dS+fQc99thkmUwmPfHEL7Vhw3oFBgZp4MDBmjUrSfPnz5Ek3XLLULVq1Uq6\nzHOtyr44rY0fTdO5s7na8kKiotp1lro/W5cfB/B6+/bt08b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+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -319,7 +320,7 @@
}
],
"source": [
- "pm.plot_posterior(trace, varnames=['difference of means','difference of stds', 'effect size'],\n",
+ "pm.plot_posterior(trace, var_names=['difference of means','difference of stds', 'effect size'],\n",
" ref_val=0,\n",
" color='#87ceeb');"
]
@@ -338,9 +339,9 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -348,8 +349,8 @@
}
],
"source": [
- "pm.forestplot(trace, varnames=['group1_mean',\n",
- " 'group2_mean']);"
+ "pm.forestplot(trace, var_names=['group1_mean',\n",
+ " 'group2_mean']);"
]
},
{
@@ -359,9 +360,9 @@
"outputs": [
{
"data": {
- "image/png": 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GqdkFmJVwkX05AXovOBs3bsSUKVPg7e2N6dOnw9LyYdW/dOkSTpw4gcaNG8PX1xfbt2+H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\nYDmq6vxavnw5nJycar0vCw4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+ "image/png": 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JE9StgHsMsDwzl7Qsu6StzE0tP28N/L5t/bXAggnsMx/4oQTRUmCzCbx+NB+htMpuiIivAruPUXYLSvfrcD3uBBZPQh0kSRPUrYBbD7i3bVn7Q97WtPw82jNyRrxXLCIeMsLi9mUzRnv9RGTmDynXEd8HzAF+GBHHjFJ8Fn99XXNaDOSRpKmmWx++fwY2joiNW5btMkb564Dt2pY9gdKKA1hJ6XIcts0I23hgWURsRLn294dOKzyaiNgkM+/JzIWZ+VrgrcCBoxRfRAnD4dfOo3TVSpJ6rFujKC8F7gHeHxEfBHajDCoZzSnARRGxF/ADyojH1/DgIJNrgFdFxJbAHcDhQPuD414aETsBV1JaW3cDP1uXg4iIDYFrIuII4ATKF4Kn8mDwrgDmRMTmTb3OAQ5uujJvoQya6c8D7iRpmutKCy4z7wFeRQmpJcCbgWP4627K4fKXAPtTrnfdCXwVeHdmfrsp8jXKNbyrgN8C36YEWKuTKLch3A68FtgzM1cDREQ2Q/k/A2wVESubf1uNcxwrgFc0dbuD0kILym0AAD+hhN11wJ7N9s8Efk4J5SuadZKkHuvasyib62QzMnNN8/vhwD6ZOaH70Drc142UoftfnOxtd4vPopSmBp9HOdjGehZlV7ooI2IGcDXlXrTDgS0ptwIs7Mb+JGltOKNA3boScM3TR14NfJbSZbiMciP3x7qxv7XVPEfyo2MU+f4Y97xJU8agz+LdqlczesPIs3p/8Owc/4WTrMZZvafCvIRde1RXZv6a8tisrsvMBWv5umMo1wYlTUMjzep98x0r+lwrTRZn9JYqMBW+LU+WXs3oDc7qXTsnPO0TB5lI/ec1uMHnjN5TkAEnTQ2OohxsPR9FKUmDYpPZ6xtslfI5iZKkKhlwkqQqGXCSpCoZcJKkKhlwkqQqGXCSpCoZcJKkKhlwkqQqGXCSpCoZcJJEeWTXFYvuZsny1f2uiiaJj+qSNO350OU6+bDlPvFhy+qmGiZA7ffEp6vW3N+TfQ+rcdLTTq3LdE9jPWzZLkpJ095IE59q8NlFKVWohglQp9vEp056OvnsouwTuyilqcNrcIPLCU+nIANOmlqc+HQwOeGpJI3DiU/r4yATSVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpUMOKnHlixfzRWL7mbJ8tX9ropUtZn9roA0nZxz1WKOPe86Np+7IbfeuYJDnrsNL9lufr+rJVVpxtDQUL/rMC3ddtsy3/gBduDCy0ddt+iulfxp2aoR182auR6nvm5Htp43m+uXLme/037DqjX3j7u/R8+ZxWYbb7DW9W133L47TNq2pH7adNM5M0ZbZxel1EPz58xi63mzAdh63mzmz5nV5xpJ9bKLUloLY7WAjr/oRk64+OYR1y1etorrly5/oAW3eJSWXrs9tp/PAbsuWJuqStOWXZR9Yhfl9OQ1OGlyjdVFacD1iQE3fS1ZvppFd61ks403YJPZ6/e7OtJAGyvg7KKUemyT2esbbFIPOMhEklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA07qMWf0lnrDZ1FKPeRsAlLvOJtAnzibwGDr9Yze4Kze0kic0VuaIpzRW+oduyiltdDrGb3BWb2libKLsk/sopyevAYnTS5n9J6CDLjpyxm9pcnjjN59FBEXAJdm5qH9roumBmf0lnpjWgRcRGwFfBrYrVl0PnBQZi6ahG0fBHw5M72pSZKmkOkyivIHwApgG+BJwDzg+HXdaERsQglOv45L0hTTtRZcROwMnAY8FrgAOBP4GLATcAPwDuBI4NDMPDki9mh+fzxwOyWAPpqZQxFxMrBRZu7dsv0hYI/MPCsibgQ+D7wI+Dvgj8CbMvOnETEXuBQ4IjPvBu6OiBPoMOAiYj7wFUrrbxbwW+CdwJLmOGYASyLinZn5bxFxBPB2Suh9aaLvmyRpcnSlBRcRs4CzgHMpraXPAR9uK/Z8SovqlIjYHvgu8HFgLrAPcDDwxgns9mDgI8AjgdOBMyNig8y8MzPf1NYd+Vjg1g63exTwMOD/NMdyHnBCZt4KvLAps0kTbi8E/i+wL7AFsArYeQLHIEmaJN3qotwJeBRwVGauyMwfUcKu1SmZeVdmDgH/BFyQmWdk5r2ZeTHwTUpQdOrszLwoM1cCn6CE0rPbC0VEAEdQgqsTc4F7gRXNdbYPZ+Yuo5TdC/hxZv53U4+jgXsmcAySpEnSrYB7DLA8M5e0LLukrcxNLT9vDfy+bf21wIIJ7DMf+CFzObAU2Ky1QETsBPwMODYzT+9wu58Engb8oekqfXlEjDYsdQtKt+VwPe6jHIckqce6FXDrUVo9rdofuLem5efRnlc04r1iEfGQERa3L5vR+vqIeBHwE0oL7MhR9vdXMvNSSvfkAcBq4BTgjFGKz+Kvr2tOl4E8kjSldOvD98/AxhGxccuy0br1AK4Dtmtb9gQebP2spHQ5DttmhG08sCwiNqJcL/tD8/vTgYXAfpn5lU4OoGVbc4H7MvP7mXkA8DLglRExb4TiiyjX94ZfOxN43ET2J0maHN0aRXkp5drT+yPig5QRiM8fo/wpwEURsRdlSP8zgdfw4CCTa4BXRcSWwB3A4ZQBHK1e2nRBXgm8D7gb+FkTMicCH8rMM9fiWH4BfDcijqK04HamdH/eQbn1AMqlvauBc4CTImJX4LKmHj5NV5L6oCstuMy8B3gVJaSWAG8GjuGvuymHy18C7E8ZBXkn8FXg3Zn57abI1yjX8K6iDNP/NiXAWp1EuQ3hduC1wJ7NoJBnAk8Ejo6IlW3/turgcPYBdgUWU4Lt5cDLMvN+4DfAhcBFwLuAb1Hui/sOZZTm+pRbJCRJPda1Z1E218lmZOaa5vfDgX0yc8cu7OtG4JjM/OJkb7tbfBbl9OWzKKXJ0/NnUTajDK+m3It2OLAl5VaAhd3YnzQonE1A6p2uBFzz9JFXA5+ldBkuo9zI/bFu7G9tRcSrgK+PUeTyzHx6r+qjwTGZM3p/8OwcsWyryZ7Nu52ze6tGXXtUV2b+mvLYrK7LzAVr+bozGH3IvzTpRprR++Y7VozzKklrY1rMJiBNtl7P6O1s3tLEOeFpnzjIZHryGpw0uZzRewoy4KYvR1FKk8cZvaUpxBm9pd7wOYmSpCoZcJKkKhlwkqQqGXCSpCoZcJKkKhlwkqQqGXCSpCoZcJKkKhlwkqQqGXBSjy1ZvporFt3NkuWr+10VqWo+qkvqIR+2LPWOD1vuEx+2PNjGmvAURp/0dKQJT1etub/j/XZj4lMnO9UgG+thy3ZRSj000oSnkrrDLkppLYzX6hlt0tO1nfB0mBOfSp2zi7JP7KKcnrwGJ00uJzydggy46csJT6XJ44Sn0hTihKdSbzjIRJJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA04ClixfzRWL7mbJ8tX9roqkSTKz3xWQ+u2cqxZz7HnXsfncDbn1zhUc8txteMl28/tdLUnraMbQ0FC/6zAt3Xbbsq688QcuvLwbm+25RXet5E/LVvVkX7Nmrsepr9uRrefN5vqly9nvtN+was39Pdn3WB49ZxabbbxBv6vRV8ftu0O/q6ApbtNN58wYbZ1dlJr25s+ZxdbzZgOw9bzZzJ8zq881kjQZ7KKsTC3feI+/6EZOuPjmnuxr8bJVXL90+QMtuMU9ajmOZ4/t53PArgv6XQ1pYNlF2Sfd6qLUxHkNThpcY3VRGnB9YsBNLUuWr2bRXSvZbOMN2GT2+v2ujqQOjRVwdlFKwCaz1zfYpMo4yESSVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTsIZvaUa+SxKTXvOJiDVydkE+mRdZhOoZdbusUz3Gb2dzftBtcxxqO5wRm9pDM7oLdXJLsoBNB2+0U73Gb2dzVtad3ZR9okTnk4dXoOTBpczek9BBtzU4oze0mByRm9pHM7oLdVnSgwyiYgTIuL0ftdDklQPuyj7xC5KSVp33iYgSZp2xrwGFxGXA6dn5tHN7+sBS4C3ZebCMV63ALgBeBlwNLAAOBN4P/B1YEfgV8ArM3NpRJwMbJSZe0fE/sB7gE8BHwUeAXwfeENm3hcRFwCXZuahbft6cmZeGRFvaPazBXAncCLwwcwct8UUEdsBnwee1iw6C3hXZt4VEbs39di7KbMF8FPgNZl5d/P6twHvao73lma/o75PkqTuGa8Fdwbw8uFfMvN+4BrgSR1uf39gV+A5wKuBhcAbgW2AJzTrR7IVsAvwRGA3YF/g78fbWURsQQm0dwAbNft9XYevnQX8GLiCEl47NPv/Qkux2cBrgWdQ3oOnDx9DROxJCeT9gTnAIcCpTWhKknpsvID7FvD0iGi9KWgWcHuH2z8xM+/MzF8Ci4HzM/OazPwTcCnwuFFe93DgA5m5PDN/SwnVToLi4ZRjuiczhzLzf4BtMvOsDl77YmAucERm/iUzbwE+CewdEQ9pyqwHfLo5ppuAS1rq9RbgpMy8JDPva/b5I2C/DvYtSZpkY3ZRZub/RMSVlK7GEyJifeDxwK873P4tLT+vBG5t+320h+3dkZl3tvz+F2DDDvZ3FXAC8POIuJjSIju5rR6j2Rq4ITNXtCy7ttnvo1qW3TBKvbYBXhgR72xZvx5wVwf7liRNsk7ug/sWpZvyBOAFwJ+Bn3e4/fYn1nb6BNuJPOl2uHVFc53tgIj4JLAn5XrZYRHxnMy8ZJztjPUAwtbrd6PVbQWl9Xd0B3WWJHVZJ6MozwCeFxGzKdeVPtHJgI0uWgk8rOX3bYZ/iIj1IuKRmXltZh6Tmc+gdCO+voPtXgcsiIjWVuUTgGWUUB+IYrvNAAAIFUlEQVTPtcBTWhdExJbNwBxJUo+N++HbXMdK4N+A+c1/++ka4LkRMS8iNgXe3rJuX+DyiHgylIChDBi5toPtnk1phR0VEbOa0ZmHAac2g2vG8xXK9bqXR8TMiNgV+C1loIskqcc6bV2cQenue0NmrulifTrxKWAp5bra+cDnWtZ9EzgJODsiVgAXUm5P+NJ4G83M5ZTRlrtQBsT8FDiX0modV2aeBxwEfIbS6jsReG9m/qSjo1LfOJu3VCefZNInPslkanAmAWmwOZvAFDTZAVfbLN+9mtF7Ks7m3c7ZvUc2HeZF1PgmdTaB5p64m8YpNj8zp8zw+EGss3pjpNm8b75jxTivkjQIbMH1iV2UY+vVjN6D0IJ7yzO3dHZvaRR2UU5BBtzU4DU4abAZcFOQATd1OJu3NLic0Vsag7N5S3XyKRuSpCoZcJKkKhlwkqQqGXCSpCoZcJKkKhlwkqQqGXCSpCoZcJKkKhlwkqQqGXBSCyc/lerho7qkhg9eluriw5b7pNcPWx60CVF7NeFpq6k+dY4Tn06ck6LWb6yHLdtFKTVGmvxU0uCyi3KaGLRvsr2a8LTV4mWruH7p8gdacIt73IIczx7bz3fiU2kC7KLsE+eDm3q8BicNHic8nYIMuKnJyU+lweKEp1KHnPxUqoeDTCRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVUy4CRJVTLgJElVMuAkSVWaMTQ01O86SJI06WzBSZKqZMBJkqpkwEmSqmTASZKqZMBJkqo0s98VmG4i4rHAl4FnAiuAM4H3ZObqEcruDRwBbANcD3wkM7/Tw+p2pNNjioj9gZOAVW2beG5mXtSDqk5IRDwZ+AawUWYuGKPcoJyncY9nAM/RVsCngd2aRecDB2XmohHKPhs4GngisAj4bGZ+tVd17VSnxxQRuzfr2s/VGzPzG92u50RExDMo7/2OwF8o9T44M/80QtlJ+3sy4HrvO8CVwLbAxsB3gSOBw1oLRcRTgNOAVwPnAi8EFkbEzpl5ZU9rPL6Ojqlx01hhMVVExD7AZ4BLKH+Uo5UbiPPU6fE0BuIcNX4AXEH5MNyAEuDHA//QWigiHt2UfR9wMuU9OCcibszMc3tZ4Q50dEzDMnOD3lVt4iLiEcCPgQ8AzwceAXwL+CqwZ1vZSf17souyhyJiJ+CpwHsz887MvAn4OHBARLSfiwOAH2fm9zJzZWZ+H/gJ8Obe1npsEzymQbIRpUX6k3HKDcR5ovPjGRgRMRe4FPiXzLw7M/8MnMCDLZ9WrwNuzMyvZOaKpjX6deCtvavx+CZ4TINiFvDPmfm5zLy3OabvADuMUHZS/55swfXW04BbMnNJy7LLKN9otgGuaSv7o7bXXwY8r6s1nLiJHBPAnIg4E3gWcA/w0cw8oSc1nYDMPBEgIsYrOhDnaQLHA4Nzju4E3tS2+LHArSMUfxrlvLS6DNirC1VbaxM8JgAi4jTgBcAQ8AXgXzPz/q5VcoKabsiTACJiBhDA/sA3Ryg+qX9Pg/wNexDNA+5oW3Z7899NOizbXq7fJnJMtwGXU/riNwPeA3w5Il7S1Rp216Ccp04N7DmKkt5HAEeNsHogz9M4x3Q3cBGlC3ML4DXAe4EDe1bBCWi6H1cDv6N0lR8xQrFJPU+24HpvRpfK9lNH9czMHwI/bFn0HxHxHWA/4JxuVKxHBuU8jWtQz1HTVf5D4NjMPH2UYgN1nsY7psy8DPjblkXnR8RxlHP1ld7UsnOZeUVErE9pwX2VEsz7jFB00s6TLbjeuo3yDaXV8O9/7rBse7l+m8gxjeRGSkthUA3KeVoXNzKFz1FEvIhynebDmXnkKMUG6jx1eEwjuZEpfK4ycygzrwYOB17VDP5pNannyYDrrUuBzdtO6i6Uk3f9CGV3alu2C/CL7lVvrXR8TBHx1mY0X6vt2ssNmEE5Tx0ZtHMUEU8HFgL7ZeZYrZaBOU+dHlNEvCoi3ta2eMqdq6aev25bPHyN8N625ZN6nuyi7KHM/E1E/AL4ZES8i/LN5Ajgi5k5FBFXA2/NzAuA44DLImIv4GzgZcDfAe/oT+1HNsFjmgV8MSKup1zn2Rt4KWV038AYxPM0lkE9RxExEzgR+FBmnjnC+p8AJ2bmv1OGnn8oIt4BfA14BvBayrFNGRM8ptXAsRFxHeW+st0pA1T271mFO3MhsG1EfAA4BpgDfBi4MDOXdvPvyYDrvb0pJ3ER5YbHkynD6qH0TW8EkJm/j4h9gU9QRhtdA7wiM6/tdYU70NExAZ+n/M99BvAY4AZgz8z8VS8r24mISGAr4CHAzIhYObyKATxPnR4PA3SOKKH7RODoiDi6bV1QRvE+AiAzb4uIv6cc37GUUYlvy8yf9bC+nZjIMZ0ZEQdRRk5uCfyJMhz/2z2s77gyc1FEvJBy8/r7KYNjzuPBof9d+3tyPjhJUpW8BidJqpIBJ0mqkgEnSaqSASdJqpIBJ0mqkgEnSaqSASdJqpIBJ0mq0v8HZxCw9Y/8scsAAAAASUVORK5CYII=\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -369,7 +370,7 @@
}
],
"source": [
- "pm.forestplot(trace, varnames=['group1_std',\n",
+ "pm.forestplot(trace, var_names=['group1_std',\n",
" 'group2_std',\n",
" 'ν_minus_one']);"
]
@@ -412,33 +413,33 @@
" \n",
" \n",
" | difference of means | \n",
- " 1.003488 | \n",
- " 0.464556 | \n",
- " 0.008673 | \n",
- " 0.110008 | \n",
- " 1.929636 | \n",
- " 2618.006985 | \n",
- " 0.999933 | \n",
+ " 1.013422 | \n",
+ " 0.447558 | \n",
+ " 0.006065 | \n",
+ " 0.086625 | \n",
+ " 1.850443 | \n",
+ " 6873.034799 | \n",
+ " 0.999837 | \n",
"
\n",
" \n",
" | difference of stds | \n",
- " 0.944355 | \n",
- " 0.450525 | \n",
- " 0.006941 | \n",
- " 0.152468 | \n",
- " 1.885434 | \n",
- " 3567.953258 | \n",
- " 0.999944 | \n",
+ " 0.945937 | \n",
+ " 0.441513 | \n",
+ " 0.005225 | \n",
+ " 0.133137 | \n",
+ " 1.843480 | \n",
+ " 6840.669245 | \n",
+ " 0.999897 | \n",
"
\n",
" \n",
" | effect size | \n",
- " 0.595406 | \n",
- " 0.289738 | \n",
- " 0.005166 | \n",
- " 0.040975 | \n",
- " 1.175411 | \n",
- " 2756.596339 | \n",
- " 0.999857 | \n",
+ " 0.600971 | \n",
+ " 0.279514 | \n",
+ " 0.003620 | \n",
+ " 0.037204 | \n",
+ " 1.128682 | \n",
+ " 7217.271311 | \n",
+ " 0.999775 | \n",
"
\n",
" \n",
"\n",
@@ -446,14 +447,14 @@
],
"text/plain": [
" mean sd mc_error hpd_2.5 hpd_97.5 \\\n",
- "difference of means 1.003488 0.464556 0.008673 0.110008 1.929636 \n",
- "difference of stds 0.944355 0.450525 0.006941 0.152468 1.885434 \n",
- "effect size 0.595406 0.289738 0.005166 0.040975 1.175411 \n",
+ "difference of means 1.013422 0.447558 0.006065 0.086625 1.850443 \n",
+ "difference of stds 0.945937 0.441513 0.005225 0.133137 1.843480 \n",
+ "effect size 0.600971 0.279514 0.003620 0.037204 1.128682 \n",
"\n",
" n_eff Rhat \n",
- "difference of means 2618.006985 0.999933 \n",
- "difference of stds 3567.953258 0.999944 \n",
- "effect size 2756.596339 0.999857 "
+ "difference of means 6873.034799 0.999837 \n",
+ "difference of stds 6840.669245 0.999897 \n",
+ "effect size 7217.271311 0.999775 "
]
},
"execution_count": 13,
@@ -462,7 +463,7 @@
}
],
"source": [
- "pm.summary(trace,varnames=['difference of means', 'difference of stds', 'effect size'])"
+ "pm.summary(trace, varnames=['difference of means', 'difference of stds', 'effect size'])"
]
},
{
@@ -489,9 +490,9 @@
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "pymc33.6",
"language": "python",
- "name": "python3"
+ "name": "pymc33_6"
},
"language_info": {
"codemirror_mode": {
@@ -503,7 +504,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.3"
+ "version": "3.6.5"
},
"latex_envs": {
"bibliofile": "biblio.bib",
@@ -514,5 +515,5 @@
}
},
"nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 2
}
diff --git a/pymc3/plots/__init__.py b/pymc3/plots/__init__.py
index e726853597..c959721c41 100644
--- a/pymc3/plots/__init__.py
+++ b/pymc3/plots/__init__.py
@@ -1,9 +1,74 @@
-from .autocorrplot import autocorrplot
-from .compareplot import compareplot
-from .forestplot import forestplot
-from .kdeplot import kdeplot
-from .posteriorplot import plot_posterior, plot_posterior_predictive_glm
-from .traceplot import traceplot
-from .energyplot import energyplot
-from .densityplot import densityplot
-from .pairplot import pairplot
+"""PyMC3 Plotting.
+
+Plots are delegated to the ArviZ library, a general purpose library for
+"exploratory analysis of Bayesian models." See https://arviz-devs.github.io/arviz/
+for details on plots.
+"""
+import functools
+import sys
+import warnings
+try:
+ import arviz as az
+except ImportError: # arviz is optional, throw exception when used
+
+ class _ImportWarner:
+ __all__ = []
+
+ def __init__(self, attr):
+ self.attr = attr
+
+ def __call__(self, *args, **kwargs):
+ raise ImportError(
+ "ArviZ is not installed. In order to use `{0.attr}`:\npip install arviz".format(self)
+ )
+
+ class _ArviZ:
+ def __getattr__(self, attr):
+ return _ImportWarner(attr)
+
+
+ az = _ArviZ()
+
+def map_args(func):
+ swaps = [
+ ('varnames', 'var_names')
+ ]
+ @functools.wraps(func)
+ def wrapped(*args, **kwargs):
+ for (old, new) in swaps:
+ if old in kwargs and new not in kwargs:
+ warnings.warn('Keyword argument `{old}` renamed to `{new}`, and will be removed in pymc3 3.8'.format(old=old, new=new))
+ kwargs[new] = kwargs.pop(old)
+ return func(*args, **kwargs)
+ return wrapped
+
+# pymc3 custom plots: override these names for custom behavior
+autocorrplot = map_args(az.plot_autocorr)
+compareplot = map_args(az.plot_compare)
+forestplot = map_args(az.plot_forest)
+kdeplot = map_args(az.plot_kde)
+plot_posterior = map_args(az.plot_posterior)
+traceplot = map_args(az.plot_trace)
+energyplot = map_args(az.plot_energy)
+densityplot = map_args(az.plot_density)
+pairplot = map_args(az.plot_pair)
+
+from .posteriorplot import plot_posterior_predictive_glm
+
+
+# Access to arviz plots: base plots provided by arviz
+for plot in az.plots.__all__:
+ setattr(sys.modules[__name__], plot, map_args(getattr(az.plots, plot)))
+
+__all__ = tuple(az.plots.__all__) + (
+ 'autocorrplot',
+ 'compareplot',
+ 'forestplot',
+ 'kdeplot',
+ 'plot_posterior',
+ 'traceplot',
+ 'energyplot',
+ 'densityplot',
+ 'pairplot',
+ 'plot_posterior_predictive_glm',
+)
diff --git a/pymc3/plots/artists.py b/pymc3/plots/artists.py
deleted file mode 100644
index 81c9dbae51..0000000000
--- a/pymc3/plots/artists.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import numpy as np
-from scipy.stats import mode
-from collections import OrderedDict
-
-from pymc3.stats import hpd
-from .kdeplot import fast_kde, kdeplot
-
-
-def _histplot_bins(column, bins=100):
- """Helper to get bins for histplot."""
- col_min = np.min(column)
- col_max = np.max(column)
- return range(col_min, col_max + 2, max((col_max - col_min) // bins, 1))
-
-
-def histplot_op(ax, data, alpha=.35):
- """Add a histogram for each column of the data to the provided axes."""
- hs = []
- for column in data.T:
- hs.append(ax.hist(column, bins=_histplot_bins(
- column), alpha=alpha, align='left'))
- ax.set_xlim(np.min(data) - 0.5, np.max(data) + 0.5)
- return hs
-
-
-def kdeplot_op(ax, data, bw, prior=None, prior_alpha=1, prior_style='--'):
- """Get a list of density and likelihood plots, if a prior is provided."""
- ls = []
- pls = []
- errored = []
- for i, d in enumerate(data.T):
- try:
- density, l, u = fast_kde(d, bw)
- x = np.linspace(l, u, len(density))
- if prior is not None:
- p = prior.logp(x).eval()
- pls.append(ax.plot(x, np.exp(p),
- alpha=prior_alpha, ls=prior_style))
-
- ls.append(ax.plot(x, density))
- except ValueError:
- errored.append(str(i))
-
- if errored:
- ax.text(.27, .47, 'WARNING: KDE plot failed for: ' + ','.join(errored),
- bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10},
- style='italic')
-
- return ls, pls
-
-
-def plot_posterior_op(trace_values, ax, bw, kde_plot, point_estimate, round_to,
- alpha_level, ref_val, rope, text_size=16, **kwargs):
- """Artist to draw posterior."""
- def format_as_percent(x, round_to=0):
- return '{0:.{1:d}f}%'.format(100 * x, round_to)
-
- def display_ref_val(ref_val):
- less_than_ref_probability = (trace_values < ref_val).mean()
- greater_than_ref_probability = (trace_values >= ref_val).mean()
- ref_in_posterior = "{} <{:g}< {}".format(
- format_as_percent(less_than_ref_probability, 1),
- ref_val,
- format_as_percent(greater_than_ref_probability, 1))
- ax.axvline(ref_val, ymin=0.02, ymax=.75, color='g',
- linewidth=4, alpha=0.65)
- ax.text(trace_values.mean(), plot_height * 0.6, ref_in_posterior,
- size=text_size, horizontalalignment='center')
-
- def display_rope(rope):
- ax.plot(rope, (plot_height * 0.02, plot_height * 0.02),
- linewidth=20, color='r', alpha=0.75)
- text_props = dict(size=text_size, horizontalalignment='center', color='r')
- ax.text(rope[0], plot_height * 0.14, rope[0], **text_props)
- ax.text(rope[1], plot_height * 0.14, rope[1], **text_props)
-
- def display_point_estimate():
- if not point_estimate:
- return
- if point_estimate not in ('mode', 'mean', 'median'):
- raise ValueError(
- "Point Estimate should be in ('mode','mean','median')")
- if point_estimate == 'mean':
- point_value = trace_values.mean()
- elif point_estimate == 'mode':
- if isinstance(trace_values[0], float):
- density, l, u = fast_kde(trace_values, bw)
- x = np.linspace(l, u, len(density))
- point_value = x[np.argmax(density)]
- else:
- point_value = mode(trace_values.round(round_to))[0][0]
- elif point_estimate == 'median':
- point_value = np.median(trace_values)
- point_text = '{point_estimate}={point_value:.{round_to}f}'.format(point_estimate=point_estimate,
- point_value=point_value, round_to=round_to)
-
- ax.text(point_value, plot_height * 0.8, point_text,
- size=text_size, horizontalalignment='center')
-
- def display_hpd():
- hpd_intervals = hpd(trace_values, alpha=alpha_level)
- ax.plot(hpd_intervals, (plot_height * 0.02,
- plot_height * 0.02), linewidth=4, color='k')
- ax.text(hpd_intervals[0], plot_height * 0.07,
- hpd_intervals[0].round(round_to),
- size=text_size, horizontalalignment='right')
- ax.text(hpd_intervals[1], plot_height * 0.07,
- hpd_intervals[1].round(round_to),
- size=text_size, horizontalalignment='left')
- ax.text((hpd_intervals[0] + hpd_intervals[1]) / 2, plot_height * 0.2,
- format_as_percent(1 - alpha_level) + ' HPD',
- size=text_size, horizontalalignment='center')
-
- def format_axes():
- ax.yaxis.set_ticklabels([])
- ax.spines['top'].set_visible(False)
- ax.spines['right'].set_visible(False)
- ax.spines['left'].set_visible(False)
- ax.spines['bottom'].set_visible(True)
- ax.yaxis.set_ticks_position('none')
- ax.xaxis.set_ticks_position('bottom')
- ax.tick_params(axis='x', direction='out', width=1, length=3,
- color='0.5', labelsize=text_size)
- ax.spines['bottom'].set_color('0.5')
-
- def set_key_if_doesnt_exist(d, key, value):
- if key not in d:
- d[key] = value
-
- if kde_plot and isinstance(trace_values[0], float):
- kdeplot(trace_values, alpha=kwargs.pop('alpha', 0.35), bw=bw, ax=ax, **kwargs)
-
- else:
- set_key_if_doesnt_exist(kwargs, 'bins', 30)
- set_key_if_doesnt_exist(kwargs, 'edgecolor', 'w')
- set_key_if_doesnt_exist(kwargs, 'align', 'right')
- set_key_if_doesnt_exist(kwargs, 'color', '#87ceeb')
- ax.hist(trace_values, **kwargs)
-
- plot_height = ax.get_ylim()[1]
-
- format_axes()
- display_hpd()
- display_point_estimate()
- if ref_val is not None:
- display_ref_val(ref_val)
- if rope is not None:
- display_rope(rope)
-
-def scale_text(figsize, text_size):
- """Scale text to figsize."""
-
- if text_size is None and figsize is not None:
- if figsize[0] <= 11:
- return 12
- else:
- return figsize[0]
- else:
- return text_size
-
-def get_trace_dict(tr, varnames):
- traces = OrderedDict()
- for v in varnames:
- vals = tr.get_values(v, combine=True, squeeze=True)
- if vals.ndim > 1:
- vals_flat = vals.reshape(vals.shape[0], -1).T
- for i, vi in enumerate(vals_flat):
- traces['_'.join([v, str(i)])] = vi
- else:
- traces[v] = vals
- return traces
-
\ No newline at end of file
diff --git a/pymc3/plots/autocorrplot.py b/pymc3/plots/autocorrplot.py
deleted file mode 100644
index 0c9d053a52..0000000000
--- a/pymc3/plots/autocorrplot.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import itertools
-import numpy as np
-
-try:
- import matplotlib.pyplot as plt
-except ImportError: # mpl is optional
- pass
-
-from .utils import get_default_varnames, get_axis
-
-
-def autocorrplot(trace, varnames=None, max_lag=100, burn=0, plot_transformed=False,
- symmetric_plot=False, ax=None, figsize=None):
- """Bar plot of the autocorrelation function for a trace.
-
- Parameters
- ----------
- trace : result of MCMC run
- varnames : list of variable names
- Variables to be plotted, if None all variable are plotted.
- Vector-value stochastics are handled automatically.
- max_lag : int
- Maximum lag to calculate autocorrelation. Defaults to 100.
- burn : int
- Number of samples to discard from the beginning of the trace.
- Defaults to 0.
- plot_transformed : bool
- Flag for plotting automatically transformed variables in addition to
- original variables (defaults to False).
- symmetric_plot : boolean
- Plot from either [0, +lag] or [-lag, lag]. Defaults to False, [-, +lag].
- ax : axes
- Matplotlib axes. Defaults to None.
- figsize : figure size tuple
- If None, size is (12, num of variables * 2) inches.
- Note this is not used if ax is supplied.
-
- Returns
- -------
- ax : matplotlib axes
- """
- def _handle_array_varnames(varname):
- if trace[0][varname].__class__ is np.ndarray:
- k = trace[varname].shape[1]
- for i in range(k):
- yield varname + '_{0}'.format(i)
- else:
- yield varname
-
- if varnames is None:
- varnames = get_default_varnames(trace.varnames, plot_transformed)
-
- varnames = list(itertools.chain.from_iterable(map(_handle_array_varnames, varnames)))
-
- nchains = trace.nchains
-
- if figsize is None:
- figsize = (12, len(varnames) * 2)
-
- ax = get_axis(ax, len(varnames), nchains,
- squeeze=False, sharex=True, sharey=True, figsize=figsize)
-
- max_lag = min(len(trace) - 1, max_lag)
-
- for i, v in enumerate(varnames):
- for j, chain in enumerate(trace.chains):
- try:
- d = np.squeeze(trace.get_values(v, chains=[chain], burn=burn,
- combine=False))
- except KeyError:
- k = int(v.split('_')[-1])
- v_use = '_'.join(v.split('_')[:-1])
- d = np.squeeze(trace.get_values(v_use, chains=[chain],
- burn=burn, combine=False)[:, k])
-
- ax[i, j].acorr(d, detrend=plt.mlab.detrend_mean, maxlags=max_lag)
-
- if j == 0:
- ax[i, j].set_ylabel("correlation")
-
- if i == len(varnames) - 1:
- ax[i, j].set_xlabel("lag")
-
- if not symmetric_plot:
- ax[i, j].set_xlim(0, max_lag)
-
- if nchains > 1:
- ax[i, j].set_title("{0} (chain {1})".format(v, chain))
- else:
- ax[i, j].set_title(v)
-
- return ax
diff --git a/pymc3/plots/compareplot.py b/pymc3/plots/compareplot.py
deleted file mode 100644
index d6f31fdcfd..0000000000
--- a/pymc3/plots/compareplot.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import numpy as np
-try:
- import matplotlib.pyplot as plt
-except ImportError: # mpl is optional
- pass
-
-
-def compareplot(comp_df, insample_dev=True, se=True, dse=True, ax=None,
- plot_kwargs=None):
- """
- Model comparison summary plot in the style of the one used in the book
- Statistical Rethinking by Richard McElreath.
-
- Parameters
- ----------
-
- comp_df: DataFrame
- the result of the `pm.compare()` function
- insample_dev : bool
- plot the in-sample deviance, that is the value of the IC without the
- penalization given by the effective number of parameters (pIC).
- Defaults to True
- se : bool
- plot the standard error of the IC estimate. Defaults to True
- dse : bool
- plot standard error of the difference in IC between each model and the
- top-ranked model. Defaults to True
- plot_kwargs : dict
- Optional arguments for plot elements. Currently accepts 'color_ic',
- 'marker_ic', 'color_insample_dev', 'marker_insample_dev', 'color_dse',
- 'marker_dse', 'ls_min_ic' 'color_ls_min_ic', 'fontsize'
- ax : axes
- Matplotlib axes. Defaults to None
-
- Returns
- -------
-
- ax : matplotlib axes
-
- """
- if ax is None:
- _, ax = plt.subplots()
-
- if plot_kwargs is None:
- plot_kwargs = {}
-
- yticks_pos, step = np.linspace(0, -1, (comp_df.shape[0] * 2) - 1,
- retstep=True)
- yticks_pos[1::2] = yticks_pos[1::2] + step / 2
-
- yticks_labels = [''] * len(yticks_pos)
-
- ic = 'WAIC'
- if ic not in comp_df.columns:
- ic = 'LOO'
-
- if dse:
- yticks_labels[0] = comp_df.index[0]
- yticks_labels[2::2] = comp_df.index[1:]
- ax.set_yticks(yticks_pos)
- ax.errorbar(x=comp_df[ic].iloc[1:],
- y=yticks_pos[1::2],
- xerr=comp_df.dSE[1:],
- color=plot_kwargs.get('color_dse', 'grey'),
- fmt=plot_kwargs.get('marker_dse', '^'))
-
- else:
- yticks_labels = comp_df.index
- ax.set_yticks(yticks_pos[::2])
-
- if se:
- ax.errorbar(x=comp_df[ic],
- y=yticks_pos[::2],
- xerr=comp_df.SE,
- color=plot_kwargs.get('color_ic', 'k'),
- fmt=plot_kwargs.get('marker_ic', 'o'),
- mfc='None',
- mew=1)
- else:
- ax.plot(comp_df[ic],
- yticks_pos[::2],
- color=plot_kwargs.get('color_ic', 'k'),
- marker=plot_kwargs.get('marker_ic', 'o'),
- mfc='None',
- mew=1,
- lw=0)
-
- if insample_dev:
- ax.plot(comp_df[ic] - (2 * comp_df['p'+ic]),
- yticks_pos[::2],
- color=plot_kwargs.get('color_insample_dev', 'k'),
- marker=plot_kwargs.get('marker_insample_dev', 'o'),
- lw=0)
-
- ax.axvline(comp_df[ic].iloc[0],
- ls=plot_kwargs.get('ls_min_ic', '--'),
- color=plot_kwargs.get('color_ls_min_ic', 'grey'))
-
- ax.set_xlabel('Deviance', fontsize=plot_kwargs.get('fontsize', 14))
- ax.set_yticklabels(yticks_labels)
- ax.set_ylim(-1 + step, 0 - step)
-
- return ax
diff --git a/pymc3/plots/densityplot.py b/pymc3/plots/densityplot.py
deleted file mode 100644
index 74d91cdee3..0000000000
--- a/pymc3/plots/densityplot.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import numpy as np
-try:
- import matplotlib.pyplot as plt
-except ImportError: # mpl is optional
- pass
-from .kdeplot import fast_kde
-from .utils import get_default_varnames
-from ..stats import hpd
-
-
-def densityplot(trace, models=None, varnames=None, alpha=0.05, point_estimate='mean',
- colors='cycle', outline=True, hpd_markers='', shade=0., bw=4.5, figsize=None,
- textsize=12, plot_transformed=False, ax=None):
- """
- Generates KDE plots for continuous variables and histograms for discretes ones.
- Plots are truncated at their 100*(1-alpha)% credible intervals. Plots are grouped
- per variable and colors assigned to models.
-
- Parameters
- ----------
- trace : trace or list of traces
- Trace(s) from an MCMC sample.
- models : list
- List with names for the models in the list of traces. Useful when
- plotting more that one trace.
- varnames: list
- List of variables to plot (defaults to None, which results in all
- variables plotted).
- alpha : float
- Alpha value for (1-alpha)*100% credible intervals (defaults to 0.05).
- point_estimate : str or None
- Plot point estimate per variable. Values should be 'mean', 'median' or None.
- Defaults to 'mean'.
- colors : list or string, optional
- List with valid matplotlib colors, one color per model. Alternative a string can be passed.
- If the string is `cycle`, it will automatically choose a color per model from matplolib's
- cycle. If a single color is passed, e.g. 'k', 'C2' or 'red' this color will be used for all
- models. Defaults to `cycle`.
- outline : boolean
- Use a line to draw KDEs and histograms. Default to True
- hpd_markers : str
- A valid `matplotlib.markers` like 'v', used to indicate the limits of the hpd interval.
- Defaults to empty string (no marker).
- shade : float
- Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1
- (opaque). Defaults to 0.
- bw : float
- Bandwidth scaling factor for the KDE. Should be larger than 0. The higher this number the
- smoother the KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule
- of thumb (the default rule used by SciPy).
- figsize : tuple
- Figure size. If None, size is (6, number of variables * 2)
- textsize : int
- Text size of the legend. Default 12.
- plot_transformed : bool
- Flag for plotting automatically transformed variables in addition to original variables
- Defaults to False.
- ax : axes
- Matplotlib axes.
-
- Returns
- -------
-
- ax : Matplotlib axes
-
- """
- if point_estimate not in ('mean', 'median', None):
- raise ValueError("Point estimate should be 'mean', 'median' or None")
-
- if not isinstance(trace, (list, tuple)):
- trace = [trace]
-
- length_trace = len(trace)
-
- if models is None:
- if length_trace > 1:
- models = ['m_{}'.format(i) for i in range(length_trace)]
- else:
- models = ['']
- elif len(models) != length_trace:
- raise ValueError(
- "The number of names for the models does not match the number of models")
-
- length_models = len(models)
-
- if colors == 'cycle':
- colors = ['C{}'.format(i % 10) for i in range(length_models)]
- elif isinstance(colors, str):
- colors = [colors for i in range(length_models)]
-
- if varnames is None:
- varnames = []
- for tr in trace:
- varnames_tmp = get_default_varnames(tr.varnames, plot_transformed)
- for v in varnames_tmp:
- if v not in varnames:
- varnames.append(v)
-
- if figsize is None:
- figsize = (6, len(varnames) * 2)
-
- fig, dplot = plt.subplots(len(varnames), 1, squeeze=False, figsize=figsize)
- dplot = dplot.flatten()
-
- for v_idx, vname in enumerate(varnames):
- for t_idx, tr in enumerate(trace):
- if vname in tr.varnames:
- vec = tr.get_values(vname)
- k = np.size(vec[0])
- if k > 1:
- vec = np.split(vec.T.ravel(), k)
- for i in range(k):
- _d_helper(vec[i], vname, colors[t_idx], bw, alpha, point_estimate,
- hpd_markers, outline, shade, dplot[v_idx])
-
- else:
- _d_helper(vec, vname, colors[t_idx], bw, alpha, point_estimate,
- hpd_markers, outline, shade, dplot[v_idx])
-
- if length_trace > 1:
- for m_idx, m in enumerate(models):
- dplot[0].plot([], label=m, c=colors[m_idx])
- dplot[0].legend(fontsize=textsize)
-
- fig.tight_layout()
-
- return dplot
-
-
-def _d_helper(vec, vname, c, bw, alpha, point_estimate, hpd_markers, outline, shade, ax):
- """
- vec : array
- 1D array from trace
- vname : str
- variable name
- c : str
- matplotlib color
- bw : float
- Bandwidth scaling factor. Should be larger than 0. The higher this number the smoother the
- KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule of thumb
- (the default used rule by SciPy).
- alpha : float
- Alpha value for (1-alpha)*100% credible intervals (defaults to 0.05).
- point_estimate : str or None
- 'mean' or 'median'
- shade : float
- Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1
- (opaque). Defaults to 0.
- ax : matplotlib axes
- """
- if vec.dtype.kind == 'f':
- density, l, u = fast_kde(vec)
- x = np.linspace(l, u, len(density))
- hpd_ = hpd(vec, alpha)
- cut = (x >= hpd_[0]) & (x <= hpd_[1])
-
- xmin = x[cut][0]
- xmax = x[cut][-1]
- ymin = density[cut][0]
- ymax = density[cut][-1]
-
- if outline:
- ax.plot(x[cut], density[cut], color=c)
- ax.plot([xmin, xmin], [-ymin/100, ymin], color=c, ls='-')
- ax.plot([xmax, xmax], [-ymax/100, ymax], color=c, ls='-')
-
- if shade:
- ax.fill_between(x, density, where=cut, color=c, alpha=shade)
-
- else:
- xmin, xmax = hpd(vec, alpha)
- bins = range(xmin, xmax+1)
- if outline:
- ax.hist(vec, bins=bins, color=c, histtype='step')
- ax.hist(vec, bins=bins, color=c, alpha=shade)
-
- if hpd_markers:
- ax.plot(xmin, 0, hpd_markers, color=c, markeredgecolor='k')
- ax.plot(xmax, 0, hpd_markers, color=c, markeredgecolor='k')
-
- if point_estimate is not None:
- if point_estimate == 'mean':
- ps = np.mean(vec)
- elif point_estimate == 'median':
- ps = np.median(vec)
- ax.plot(ps, -0.001, 'o', color=c, markeredgecolor='k')
-
- ax.set_yticks([])
- ax.set_title(vname)
- for pos in ['left', 'right', 'top']:
- ax.spines[pos].set_visible(0)
diff --git a/pymc3/plots/energyplot.py b/pymc3/plots/energyplot.py
deleted file mode 100644
index 5fbba176ce..0000000000
--- a/pymc3/plots/energyplot.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import warnings
-
-import numpy as np
-try:
- import matplotlib.pyplot as plt
-except ImportError: # mpl is optional
- pass
-from .kdeplot import kdeplot
-
-
-def energyplot(trace, kind='kde', figsize=None, ax=None, legend=True, shade=0.35, bw=4.5,
- frame=True, kwargs_shade=None, **kwargs):
- """Plot energy transition distribution and marginal energy distribution in
- order to diagnose poor exploration by HMC algorithms.
-
- Parameters
- ----------
-
- trace : result of MCMC run
- kind : str
- Type of plot to display (kde or histogram)
- figsize : figure size tuple
- If None, size is (8 x 6)
- ax : axes
- Matplotlib axes.
- legend : bool
- Flag for plotting legend (defaults to True)
- shade : float
- Alpha blending value for the shaded area under the curve, between 0
- (no shade) and 1 (opaque). Defaults to 0.35
- bw : float
- Bandwidth scaling factor for the KDE. Should be larger than 0. The higher this number the
- smoother the KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule
- of thumb (the default rule used by SciPy). Only works if `kind='kde'`.
- frame : bool
- Flag for plotting frame around figure.
- kwargs_shade : dicts, optional
- Additional keywords passed to `fill_between` (to control the shade)
- Returns
- -------
-
- ax : matplotlib axes
- """
-
- if ax is None:
- _, ax = plt.subplots(figsize=figsize)
-
- try:
- energy = trace['energy']
- except KeyError:
- warnings.warn('There is no energy information in the passed trace.')
- return ax
-
- series = [('Marginal energy distribution', energy - energy.mean()),
- ('Energy transition distribution', np.diff(energy))]
-
- if figsize is None:
- figsize = (8, 6)
-
- if kwargs_shade is None:
- kwargs_shade = {}
-
- if kind == 'kde':
- for label, value in series:
- kdeplot(value, label=label, shade=shade, bw=bw, ax=ax, kwargs_shade=kwargs_shade,
- **kwargs)
-
- elif kind == 'hist':
- for label, value in series:
- ax.hist(value, alpha=shade, label=label, **kwargs)
-
- else:
- raise ValueError('Plot type {} not recognized.'.format(kind))
-
- ax.set_xticks([])
- ax.set_yticks([])
-
- if not frame:
- for spine in ax.spines.values():
- spine.set_visible(False)
-
- if legend:
- ax.legend()
-
- return ax
diff --git a/pymc3/plots/forestplot.py b/pymc3/plots/forestplot.py
deleted file mode 100644
index bf103903ff..0000000000
--- a/pymc3/plots/forestplot.py
+++ /dev/null
@@ -1,328 +0,0 @@
-try:
- import matplotlib.pyplot as plt
- from matplotlib import gridspec
-except ImportError: # mpl is optional
- pass
-import numpy as np
-from pymc3.diagnostics import gelman_rubin
-from pymc3.stats import quantiles, hpd, dict2pd
-from .utils import identity_transform, get_default_varnames
-
-def _var_str(name, shape):
- """Return a sequence of strings naming the element of the tallyable object.
-
- :Example:
- >>> _var_str('theta', (4,))
- ['theta[0]', 'theta[1]', 'theta[2]', 'theta[3]']
- """
- size = np.prod(shape)
- ind = (np.indices(shape)).reshape(-1, size)
- names = ['[' + ','.join(map(str, i)) + ']' for i in zip(*ind)]
- names[0] = '%s %s' % (name, names[0])
- return names
-
-
-def _plot_tree(ax, y, ntiles, show_quartiles, c, plot_kwargs):
- """Helper to plot errorbars for the forestplot.
-
- Parameters
- ----------
- ax: Matplotlib.Axes
- y: float
- y value to add error bar to
- ntiles: iterable
- A list or array of length 5 or 3
- show_quartiles: boolean
- Whether to plot the interquartile range
- c : string
- color
- Returns
- -------
-
- Matplotlib.Axes with a single error bar added
-
- """
- if show_quartiles:
- # Plot median
- ax.plot(ntiles[2], y, color=c,
- marker=plot_kwargs.get('marker', 'o'),
- markersize=plot_kwargs.get('markersize', 4))
- # Plot quartile interval
- ax.errorbar(x=(ntiles[1], ntiles[3]), y=(y, y),
- linewidth=plot_kwargs.get('linewidth', 2),
- color=c)
-
- else:
- # Plot median
- ax.plot(ntiles[1], y, marker=plot_kwargs.get('marker', 'o'),
- color=c, markersize=plot_kwargs.get('markersize', 4))
-
- # Plot outer interval
- ax.errorbar(x=(ntiles[0], ntiles[-1]), y=(y, y),
- linewidth=int(plot_kwargs.get('linewidth', 2)/2),
- color=c)
-
- return ax
-
-
-def forestplot(trace, models=None, varnames=None, transform=identity_transform,
- alpha=0.05, quartiles=True, rhat=True, main=None, xtitle=None,
- xlim=None, ylabels=None, colors='C0', chain_spacing=0.1, vline=0,
- gs=None, plot_transformed=False, plot_kwargs=None):
- """
- Forest plot (model summary plot).
-
- Generates a "forest plot" of 100*(1-alpha)% credible intervals from a trace
- or list of traces.
-
- Parameters
- ----------
-
- trace : trace or list of traces
- Trace(s) from an MCMC sample.
- models : list (optional)
- List with names for the models in the list of traces. Useful when
- plotting more that one trace.
- varnames: list
- List of variables to plot (defaults to None, which results in all
- variables plotted).
- transform : callable
- Function to transform data (defaults to identity)
- alpha : float, optional
- Alpha value for (1-alpha)*100% credible intervals (defaults to 0.05).
- quartiles : bool, optional
- Flag for plotting the interquartile range, in addition to the
- (1-alpha)*100% intervals (defaults to True).
- rhat : bool, optional
- Flag for plotting Gelman-Rubin statistics. Requires 2 or more chains
- (defaults to True).
- main : string, optional
- Title for main plot. Passing False results in titles being suppressed;
- passing None (default) results in default titles.
- xtitle : string, optional
- Label for x-axis. Defaults to no label
- xlim : list or tuple, optional
- Range for x-axis. Defaults to matplotlib's best guess.
- ylabels : list or array, optional
- User-defined labels for each variable. If not provided, the node
- __name__ attributes are used.
- colors : list or string, optional
- list with valid matplotlib colors, one color per model. Alternative a
- string can be passed. If the string is `cycle `, it will automatically
- chose a color per model from the matyplolib's cycle. If a single color
- is passed, eg 'k', 'C2', 'red' this color will be used for all models.
- Defauls to 'C0' (blueish in most matplotlib styles)
- chain_spacing : float, optional
- Plot spacing between chains (defaults to 0.1).
- vline : numeric, optional
- Location of vertical reference line (defaults to 0).
- gs : GridSpec
- Matplotlib GridSpec object. Defaults to None.
- plot_transformed : bool
- Flag for plotting automatically transformed variables in addition to
- original variables (defaults to False).
- plot_kwargs : dict
- Optional arguments for plot elements. Currently accepts 'fontsize',
- 'linewidth', 'marker', and 'markersize'.
-
- Returns
- -------
-
- gs : matplotlib GridSpec
-
- """
- if plot_kwargs is None:
- plot_kwargs = {}
-
- if not isinstance(trace, (list, tuple)):
- trace = [trace]
-
- if models is None:
- if len(trace) > 1:
- models = ['m_{}'.format(i) for i in range(len(trace))]
- else:
- models = ['']
- elif len(models) != len(trace):
- raise ValueError("The number of names for the models does not match "
- "the number of models")
-
- if colors == 'cycle':
- colors = ['C{}'.format(i % 10) for i in range(len(models))]
- elif isinstance(colors, str):
- colors = [colors for i in range(len(models))]
-
- # Quantiles to be calculated
- if quartiles:
- qlist = [100 * alpha / 2, 25, 50, 75, 100 * (1 - alpha / 2)]
- else:
- qlist = [100 * alpha / 2, 50, 100 * (1 - alpha / 2)]
-
- nchains = [tr.nchains for tr in trace]
-
- if varnames is None:
- varnames = []
- for idx, tr in enumerate(trace):
- varnames_tmp = get_default_varnames(tr.varnames, plot_transformed)
- for v in varnames_tmp:
- if v not in varnames:
- varnames.append(v)
-
- plot_rhat = [rhat and nch > 1 for nch in nchains]
- # Empty list for y-axis labels
- if gs is None:
- # Initialize plot
- if np.any(plot_rhat):
- gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])
- gr_plot = plt.subplot(gs[1])
- gr_plot.set_xticks((1.0, 1.5, 2.0), ("1", "1.5", "2+"))
- gr_plot.set_xlim(0.9, 2.1)
- gr_plot.set_yticks([])
- gr_plot.set_title('R-hat')
- else:
- gs = gridspec.GridSpec(1, 1)
-
- # Subplot for confidence intervals
- interval_plot = plt.subplot(gs[0])
-
- trace_quantiles = []
- hpd_intervals = []
- for tr in trace:
- trace_quantiles.append(quantiles(tr, qlist, transform=transform,
- squeeze=False))
- hpd_intervals.append(hpd(tr, alpha, transform=transform,
- squeeze=False))
-
- labels = []
- var = 0
- all_quants = []
- bands = [0.05, 0] * len(varnames)
- var_old = 0.5
- for v_idx, v in enumerate(varnames):
- for h, tr in enumerate(trace):
- if v not in tr.varnames:
- labels.append(models[h] + ' ' + v)
- var += 1
- else:
- for j, chain in enumerate(tr.chains):
- var_quantiles = trace_quantiles[h][chain][v]
-
- quants = [var_quantiles[vq] for vq in qlist]
- var_hpd = hpd_intervals[h][chain][v].T
-
- # Substitute HPD interval for quantile
- quants[0] = var_hpd[0].T
- quants[-1] = var_hpd[1].T
-
- # Ensure x-axis contains range of current interval
- all_quants.extend(np.ravel(quants))
-
- # Number of elements in current variable
- value = tr.get_values(v, chains=[chain])[0]
- k = np.size(value)
-
- # Append variable name(s) to list
- if j == 0:
- if k > 1:
- names = _var_str(v, np.shape(value))
- names[0] = models[h] + ' ' + names[0]
- labels += names
- else:
- labels.append(models[h] + ' ' + v)
-
- # Add spacing for each chain, if more than one
- offset = [0] + [(chain_spacing * ((i + 2) / 2)) *
- (-1) ** i for i in range(nchains[h] - 1)]
-
- # Y coordinate with offset
- y = - var + offset[j]
-
- # Deal with multivariate nodes
-
- if k > 1:
- qs = np.moveaxis(np.array(quants), 0, -1).squeeze()
- for q in qs.reshape(-1, len(quants)):
- # Multiple y values
- interval_plot = _plot_tree(interval_plot, y, q,
- quartiles, colors[h],
- plot_kwargs)
- y -= 1
- else:
- interval_plot = _plot_tree(interval_plot, y, quants,
- quartiles, colors[h],
- plot_kwargs)
-
- # Genenerate Gelman-Rubin plot
- if plot_rhat[h] and v in tr.varnames:
- R = gelman_rubin(tr, [v])
- if k > 1:
- Rval = dict2pd(R, 'rhat').values
- gr_plot.plot([min(r, 2) for r in Rval],
- [-(j + var) for j in range(k)], 'o',
- color=colors[h], markersize=4)
- else:
- gr_plot.plot(min(R[v], 2), -var, 'o', color=colors[h],
- markersize=4)
- var += k
-
- if len(trace) > 1:
- interval_plot.axhspan(var_old, y - chain_spacing - 0.5,
- facecolor='k', alpha=bands[v_idx])
- if np.any(plot_rhat):
- gr_plot.axhspan(var_old, y - chain_spacing - 0.5,
- facecolor='k', alpha=bands[v_idx])
- var_old = y - chain_spacing - 0.5
-
- if ylabels is not None:
- labels = ylabels
-
- # Update margins
- left_margin = np.max([len(x) for x in labels]) * 0.015
- gs.update(left=left_margin, right=0.95, top=0.9, bottom=0.05)
-
- # Define range of y-axis for forestplot and R-hat
- interval_plot.set_ylim(- var + 0.5, 0.5)
- if np.any(plot_rhat):
- gr_plot.set_ylim(- var + 0.5, 0.5)
-
- plotrange = [np.min(all_quants), np.max(all_quants)]
- datarange = plotrange[1] - plotrange[0]
- interval_plot.set_xlim(plotrange[0] - 0.05 * datarange,
- plotrange[1] + 0.05 * datarange)
-
- # Add variable labels
- interval_plot.set_yticks([- l for l in range(len(labels))])
- interval_plot.set_yticklabels(labels,
- fontsize=plot_kwargs.get('fontsize', None))
-
- # Add title
- if main is None:
- plot_title = "{:.0f}% Credible Intervals".format((1 - alpha) * 100)
- elif main:
- plot_title = main
- else:
- plot_title = ""
-
- interval_plot.set_title(plot_title,
- fontsize=plot_kwargs.get('fontsize', None))
-
- # Add x-axis label
- if xtitle is not None:
- interval_plot.set_xlabel(xtitle)
-
- # Constrain to specified range
- if xlim is not None:
- interval_plot.set_xlim(*xlim)
-
- # Remove ticklines on y-axes
- for ticks in interval_plot.yaxis.get_major_ticks():
- ticks.tick1On = False
- ticks.tick2On = False
-
- for loc, spine in interval_plot.spines.items():
- if loc in ['left', 'right']:
- spine.set_color('none') # don't draw spine
-
- # Reference line
- interval_plot.axvline(vline, color='k', linestyle=':')
-
- return gs
diff --git a/pymc3/plots/kdeplot.py b/pymc3/plots/kdeplot.py
deleted file mode 100644
index be8fa48c1e..0000000000
--- a/pymc3/plots/kdeplot.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import numpy as np
-from scipy.signal import gaussian, convolve
-from scipy.stats import entropy
-
-try:
- import matplotlib.pyplot as plt
-except ImportError: # mpl is optional
- pass
-
-
-def kdeplot(values, label=None, shade=0, bw=4.5, ax=None, kwargs_shade=None, **kwargs):
- """
- 1D KDE plot taking into account boundary conditions
-
- Parameters
- ----------
- values : array-like
- Values to plot
- label : string
- Text to include as part of the legend
- shade : float
- Alpha blending value for the shaded area under the curve, between 0
- (no shade) and 1 (opaque). Defaults to 0
- bw : float
- Bandwidth scaling factor. Should be larger than 0. The higher this number the smoother the
- KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule of thumb
- (the default rule used by SciPy).
- ax : matplotlib axes
- kwargs_shade : dicts, optional
- Additional keywords passed to `matplotlib.axes.Axes.fill_between`
- (to control the shade)
- Returns
- ----------
- ax : matplotlib axes
-
- """
- if ax is None:
- _, ax = plt.subplots()
-
- if kwargs_shade is None:
- kwargs_shade = {}
-
- density, l, u = fast_kde(values, bw)
- x = np.linspace(l, u, len(density))
- ax.plot(x, density, label=label, **kwargs)
- ax.set_ylim(0, auto=True)
- if shade:
- ax.fill_between(x, density, alpha=shade, **kwargs_shade)
- return ax
-
-
-def fast_kde(x, bw=4.5):
- """
- A fft-based Gaussian kernel density estimate (KDE)
- The code was adapted from https://github.com/mfouesneau/faststats
-
- Parameters
- ----------
- x : Numpy array or list
- bw : float
- Bandwidth scaling factor for the KDE. Should be larger than 0. The higher this number the
- smoother the KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule
- of thumb (the default rule used by SciPy).
-
- Returns
- -------
- density: A gridded 1D KDE of the input points (x)
- xmin: minimum value of x
- xmax: maximum value of x
- """
- x = np.asarray(x, dtype=float)
- x = x[np.isfinite(x)]
- n = len(x)
- nx = 200
-
- xmin, xmax = np.min(x), np.max(x)
-
- dx = (xmax - xmin) / (nx - 1)
- std_x = entropy((x - xmin) / dx) * bw
- if ~np.isfinite(std_x):
- std_x = 0.
- grid, _ = np.histogram(x, bins=nx)
-
- scotts_factor = n ** (-0.2)
- kern_nx = int(scotts_factor * 2 * np.pi * std_x)
- kernel = gaussian(kern_nx, scotts_factor * std_x)
-
- npad = min(nx, 2 * kern_nx)
- grid = np.concatenate([grid[npad: 0: -1], grid, grid[nx: nx - npad: -1]])
- density = convolve(grid, kernel, mode='same')[npad: npad + nx]
-
- norm_factor = n * dx * (2 * np.pi * std_x ** 2 * scotts_factor ** 2) ** 0.5
-
- density = density / norm_factor
-
- return density, xmin, xmax
diff --git a/pymc3/plots/pairplot.py b/pymc3/plots/pairplot.py
deleted file mode 100644
index 516e1117f2..0000000000
--- a/pymc3/plots/pairplot.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import warnings
-
-try:
- import matplotlib.pyplot as plt
- from matplotlib import gridspec
-except ImportError: # mpl is optional
- pass
-from ..util import get_default_varnames, is_transformed_name, get_untransformed_name
-from .artists import get_trace_dict, scale_text
-
-
-def pairplot(trace, varnames=None, figsize=None, text_size=None,
- gs=None, ax=None, hexbin=False, plot_transformed=False,
- divergences=False, kwargs_divergence=None,
- sub_varnames=None, **kwargs):
- """
- Plot a scatter or hexbin matrix of the sampled parameters.
-
- Parameters
- ----------
-
- trace : result of MCMC run
- varnames : list of variable names
- Variables to be plotted, if None all variable are plotted
- figsize : figure size tuple
- If None, size is (8 + numvars, 8 + numvars)
- text_size: int
- Text size for labels
- gs : Grid spec
- Matplotlib Grid spec.
- ax: axes
- Matplotlib axes
- hexbin : Boolean
- If True draws an hexbin plot
- plot_transformed : bool
- Flag for plotting automatically transformed variables in addition to
- original variables (defaults to False). Applies when varnames = None.
- When a list of varnames is passed, transformed variables can be passed
- using their names.
- divergences : Boolean
- If True divergences will be plotted in a diferent color
- kwargs_divergence : dicts, optional
- Aditional keywords passed to ax.scatter for divergences
- sub_varnames : list
- Aditional varnames passed for plotting subsets of multidimensional
- variables
- Returns
- -------
-
- ax : matplotlib axes
- gs : matplotlib gridspec
-
- """
- if varnames is None:
- if plot_transformed:
-
- varnames_copy = list(trace.varnames)
- remove = [get_untransformed_name(var) for var in trace.varnames
- if is_transformed_name(var)]
-
- try:
- [varnames_copy.remove(i) for i in remove]
- varnames = varnames_copy
- except ValueError:
- varnames = varnames_copy
-
- trace_dict = get_trace_dict(
- trace, get_default_varnames(
- varnames, plot_transformed))
-
- else:
- trace_dict = get_trace_dict(
- trace, get_default_varnames(
- trace.varnames, plot_transformed))
-
- if sub_varnames is None:
- varnames = list(trace_dict.keys())
-
- else:
- trace_dict = get_trace_dict(
- trace, get_default_varnames(
- trace.varnames, True))
- varnames = sub_varnames
-
- else:
- trace_dict = get_trace_dict(trace, varnames)
- varnames = list(trace_dict.keys())
-
- if text_size is None:
- text_size = scale_text(figsize, text_size=text_size)
-
- if kwargs_divergence is None:
- kwargs_divergence = {}
-
- numvars = len(varnames)
-
- if figsize is None:
- figsize = (8 + numvars, 8 + numvars)
-
- if numvars < 2:
- raise Exception(
- 'Number of variables to be plotted must be 2 or greater.')
-
- if numvars == 2 and ax is not None:
- if hexbin:
- ax.hexbin(trace_dict[varnames[0]],
- trace_dict[varnames[1]], mincnt=1, **kwargs)
- else:
- ax.scatter(trace_dict[varnames[0]],
- trace_dict[varnames[1]], **kwargs)
-
- if divergences:
- try:
- divergent = trace['diverging']
- except KeyError:
- warnings.warn('No divergences were found.')
-
- diverge = (divergent == 1)
- ax.scatter(trace_dict[varnames[0]][diverge],
- trace_dict[varnames[1]][diverge], **kwargs_divergence)
- ax.set_xlabel('{}'.format(varnames[0]),
- fontsize=text_size)
- ax.set_ylabel('{}'.format(
- varnames[1]), fontsize=text_size)
- ax.tick_params(labelsize=text_size)
-
- if gs is None and ax is None:
- plt.figure(figsize=figsize)
- gs = gridspec.GridSpec(numvars - 1, numvars - 1)
-
- for i in range(0, numvars - 1):
- var1 = trace_dict[varnames[i]]
-
- for j in range(i, numvars - 1):
- var2 = trace_dict[varnames[j + 1]]
-
- ax = plt.subplot(gs[j, i])
-
- if hexbin:
- ax.hexbin(var1, var2, mincnt=1, **kwargs)
- else:
- ax.scatter(var1, var2, **kwargs)
-
- if divergences:
- try:
- divergent = trace['diverging']
- except KeyError:
- warnings.warn('No divergences were found.')
- return ax
-
- diverge = (divergent == 1)
- ax.scatter(var1[diverge],
- var2[diverge],
- **kwargs_divergence)
-
- if j + 1 != numvars - 1:
- ax.set_xticks([])
- else:
- ax.set_xlabel('{}'.format(varnames[i]),
- fontsize=text_size)
- if i != 0:
- ax.set_yticks([])
- else:
- ax.set_ylabel('{}'.format(
- varnames[j + 1]), fontsize=text_size)
-
- ax.tick_params(labelsize=text_size)
-
- plt.tight_layout()
- return ax, gs
diff --git a/pymc3/plots/posteriorplot.py b/pymc3/plots/posteriorplot.py
index d2ae8e5151..102865dc51 100644
--- a/pymc3/plots/posteriorplot.py
+++ b/pymc3/plots/posteriorplot.py
@@ -4,122 +4,6 @@
pass
import numpy as np
-from .artists import plot_posterior_op, get_trace_dict, scale_text
-
-from .utils import identity_transform, get_default_varnames
-
-
-def plot_posterior(trace, varnames=None, transform=identity_transform, figsize=None, text_size=None,
- alpha_level=0.05, round_to=3, point_estimate='mean', rope=None,
- ref_val=None, kde_plot=False, plot_transformed=False, bw=4.5, ax=None, **kwargs):
- """Plot Posterior densities in style of John K. Kruschke book.
-
- Parameters
- ----------
-
- trace : result of MCMC run
- varnames : list of variable names
- Variables to be plotted, if None all variable are plotted
- transform : callable
- Function to transform data (defaults to identity)
- figsize : figure size tuple
- If None, size is (12, num of variables * 2) inch
- text_size : int
- Text size of the point_estimates, axis ticks, and HPD (Default:16)
- alpha_level : float
- Defines range for High Posterior Density
- round_to : int
- Controls formatting for floating point numbers
- point_estimate: str
- Must be in ('mode', 'mean', 'median')
- rope: list or numpy array
- Lower and upper values of the Region Of Practical Equivalence
- ref_val: float or list-like
- display the percentage below and above the values in ref_val.
- If a list is provided, its length should match the number of variables.
- kde_plot: bool
- if True plot a KDE instead of a histogram. For discrete variables this
- argument is ignored.
- plot_transformed : bool
- Flag for plotting automatically transformed variables in addition to
- original variables (defaults to False).
- bw : float
- Bandwidth scaling factor for the KDE. Should be larger than 0. The higher this number the
- smoother the KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule
- of thumb (the default rule used by SciPy). Only works if `kde_plot` is True.
- ax : axes
- Matplotlib axes. Defaults to None.
- **kwargs
- Passed as-is to plt.hist() or plt.plot() function, depending on the
- value of the argument kde_plot
- Some defaults are added, if not specified
- color='#87ceeb' will match the style in the book
-
- Returns
- -------
-
- ax : matplotlib axes
-
- """
-
- def create_axes_grid(figsize, traces):
- l_trace = len(traces)
- if l_trace == 1:
- fig, ax = plt.subplots(figsize=figsize)
- else:
- n = np.ceil(l_trace / 2.0).astype(int)
- if figsize is None:
- figsize = (12, n * 2.5)
- fig, ax = plt.subplots(n, 2, figsize=figsize)
- ax = ax.reshape(2 * n)
- if l_trace % 2 == 1:
- ax[-1].set_axis_off()
- ax = ax[:-1]
- return fig, ax
-
- if isinstance(trace, np.ndarray):
- if figsize is None:
- figsize = (6, 2)
- if ax is None:
- fig, ax = plt.subplots(figsize=figsize)
-
-
- plot_posterior_op(transform(trace), ax=ax, bw=bw, kde_plot=kde_plot,
- point_estimate=point_estimate, round_to=round_to, alpha_level=alpha_level,
- ref_val=ref_val, rope=rope, text_size=scale_text(figsize, text_size), **kwargs)
-
- else:
- if varnames is None:
- varnames = get_default_varnames(trace.varnames, plot_transformed)
-
- trace_dict = get_trace_dict(trace, varnames)
-
- if ax is None:
- fig, ax = create_axes_grid(figsize, trace_dict)
-
- var_num = len(trace_dict)
- if ref_val is None:
- ref_val = [None] * var_num
- elif np.isscalar(ref_val):
- ref_val = [ref_val for _ in range(var_num)]
-
- if rope is None:
- rope = [None] * var_num
- elif np.ndim(rope) == 1:
- rope = [rope] * var_num
-
- for idx, (a, v) in enumerate(zip(np.atleast_1d(ax), trace_dict)):
- tr_values = transform(trace_dict[v])
- plot_posterior_op(tr_values, ax=a, bw=bw, kde_plot=kde_plot,
- point_estimate=point_estimate, round_to=round_to,
- alpha_level=alpha_level, ref_val=ref_val[idx],
- rope=rope[idx], text_size=scale_text(figsize, text_size), **kwargs)
- a.set_title(v, fontsize=scale_text(figsize, text_size))
-
- plt.tight_layout()
- return ax
-
-
def plot_posterior_predictive_glm(trace, eval=None, lm=None, samples=30, **kwargs):
"""Plot posterior predictive of a linear model.
:Arguments:
diff --git a/pymc3/plots/traceplot.py b/pymc3/plots/traceplot.py
deleted file mode 100644
index 54e99327ce..0000000000
--- a/pymc3/plots/traceplot.py
+++ /dev/null
@@ -1,149 +0,0 @@
-try:
- import matplotlib.pyplot as plt
-except ImportError: # mpl is optional
- pass
-import numpy as np
-
-from .artists import histplot_op, kdeplot_op
-from .utils import identity_transform, get_default_varnames, get_axis, make_2d
-
-
-def traceplot(trace, varnames=None, transform=identity_transform, figsize=None, lines=None,
- combined=False, plot_transformed=False, grid=False, alpha=0.35, priors=None,
- prior_alpha=1, prior_style='--', bw=4.5, ax=None, live_plot=False,
- skip_first=0, refresh_every=100, roll_over=1000):
- """Plot samples histograms and values.
-
- Parameters
- ----------
-
- trace : result of MCMC run
- varnames : list of variable names
- Variables to be plotted, if None all variable are plotted
- transform : callable
- Function to transform data (defaults to identity)
- figsize : figure size tuple
- If None, size is (12, num of variables * 2) inch
- lines : dict
- Dictionary of variable name / value to be overplotted as vertical
- lines to the posteriors and horizontal lines on sample values
- e.g. mean of posteriors, true values of a simulation.
- If an array of values, line colors are matched to posterior colors.
- Otherwise, a default red line
- combined : bool
- Flag for combining multiple chains into a single chain. If False
- (default), chains will be plotted separately.
- plot_transformed : bool
- Flag for plotting automatically transformed variables in addition to
- original variables (defaults to False).
- grid : bool
- Flag for adding gridlines to histogram. Defaults to True.
- alpha : float
- Alpha value for plot line. Defaults to 0.35.
- priors : iterable of PyMC distributions
- PyMC prior distribution(s) to be plotted alongside posterior. Defaults
- to None (no prior plots).
- prior_alpha : float
- Alpha value for prior plot. Defaults to 1.
- prior_style : str
- Line style for prior plot. Defaults to '--' (dashed line).
- bw : float
- Bandwidth scaling factor for the KDE. Should be larger than 0. The higher this number the
- smoother the KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule
- of thumb (the default rule used by SciPy).
- ax : axes
- Matplotlib axes. Accepts an array of axes, e.g.:
- live_plot: bool
- Flag for updating the current figure while sampling
- skip_first : int
- Number of first samples not shown in plots (burn-in). This affects
- frequency and stream plots.
- refresh_every : int
- Period of plot updates (in sample number)
- roll_over : int
- Width of the sliding window for the sample stream plots: last roll_over
- samples are shown (no effect on frequency plots).
-
- >>> fig, axs = plt.subplots(3, 2) # 3 RVs
- >>> pymc3.traceplot(trace, ax=axs)
-
- Creates own axes by default.
-
- Returns
- -------
-
- ax : matplotlib axes
-
- """
- trace = trace[skip_first:]
-
- if varnames is None:
- varnames = get_default_varnames(trace.varnames, plot_transformed)
-
- if figsize is None:
- figsize = (12, len(varnames) * 2)
-
- ax = get_axis(ax, len(varnames), 2, squeeze=False, figsize=figsize)
-
- for i, v in enumerate(varnames):
- if priors is not None:
- prior = priors[i]
- else:
- prior = None
- first_time = True
- for d in trace.get_values(v, combine=combined, squeeze=False):
- d = np.squeeze(transform(d))
- d = make_2d(d)
- d_stream = d
- x0 = 0
- if live_plot:
- x0 = skip_first
- if first_time:
- ax[i, 0].cla()
- ax[i, 1].cla()
- first_time = False
- if roll_over is not None:
- if len(d) >= roll_over:
- x0 = len(d) - roll_over + skip_first
- d_stream = d[-roll_over:]
- width = len(d_stream)
- if d.dtype.kind == 'i':
- hist_objs = histplot_op(ax[i, 0], d, alpha=alpha)
- colors = [h[-1][0].get_facecolor() for h in hist_objs]
- else:
- artists = kdeplot_op(ax[i, 0], d, bw, prior, prior_alpha, prior_style)[0]
- colors = [a[0].get_color() for a in artists]
- ax[i, 0].set_title(str(v))
- ax[i, 0].grid(grid)
- ax[i, 1].set_title(str(v))
- ax[i, 1].plot(range(x0, x0 + width), d_stream, alpha=alpha)
-
- ax[i, 0].set_ylabel("Frequency")
- ax[i, 1].set_ylabel("Sample value")
-
- if lines:
- try:
- if isinstance(lines[v], (float, int)):
- line_values, colors = [lines[v]], ['r']
- else:
- line_values = np.atleast_1d(lines[v]).ravel()
- if len(colors) != len(line_values):
- raise AssertionError("An incorrect number of lines was specified for "
- "'{}'. Expected an iterable of length {} or to "
- " a scalar".format(v, len(colors)))
- for c, l in zip(colors, line_values):
- ax[i, 0].axvline(x=l, color=c, lw=1.5, alpha=0.75)
- ax[i, 1].axhline(y=l, color=c,
- lw=1.5, alpha=alpha)
- except KeyError:
- pass
- if live_plot:
- for j in [0, 1]:
- ax[i, j].relim()
- ax[i, j].autoscale_view(True, True, True)
- ax[i, 1].set_xlim(x0, x0 + width)
- ax[i, 0].set_ylim(bottom=0)
- if live_plot:
- ax[0, 0].figure.canvas.draw()
- plt.tight_layout()
- return ax
diff --git a/pymc3/plots/utils.py b/pymc3/plots/utils.py
deleted file mode 100644
index 9b96931936..0000000000
--- a/pymc3/plots/utils.py
+++ /dev/null
@@ -1,43 +0,0 @@
-try:
- import matplotlib.pyplot as plt
-except ImportError: # mpl is optional
- pass
-import numpy as np
-# plotting utilities can all be in this namespace
-from ..util import get_default_varnames # pylint: disable=unused-import
-
-
-def identity_transform(x):
- """f(x) = x"""
- return x
-
-
-def get_axis(ax, default_rows, default_columns, **default_kwargs):
- """Verifies the provided axis is of the correct shape, and creates one if needed.
-
- Args:
- ax: matplotlib axis or None
- default_rows: int, expected rows in axis
- default_columns: int, expected columns in axis
- **default_kwargs: keyword arguments to pass to plt.subplot
-
- Returns:
- axis, or raises an error
- """
-
- default_shape = (default_rows, default_columns)
- if ax is None:
- _, ax = plt.subplots(*default_shape, **default_kwargs)
- elif ax.shape != default_shape:
- raise ValueError('Subplots with shape %r required' % (default_shape,))
- return ax
-
-
-def make_2d(a):
- """Ravel the dimensions after the first."""
- a = np.atleast_2d(a.T).T
- # flatten out dimensions beyond the first
- n = a.shape[0]
- newshape = np.product(a.shape[1:]).astype(int)
- a = a.reshape((n, newshape), order='F')
- return a
diff --git a/pymc3/tests/test_distributions_timeseries.py b/pymc3/tests/test_distributions_timeseries.py
index f8679d7f1b..11660384db 100644
--- a/pymc3/tests/test_distributions_timeseries.py
+++ b/pymc3/tests/test_distributions_timeseries.py
@@ -27,7 +27,7 @@ def test_AR():
# AR1 + constant
with Model() as t:
- y = AR('y', [0.3, phi], sigma=1, shape=len(data), constant=True)
+ y = AR('y', np.hstack((0.3, phi)), sigma=1, shape=len(data), constant=True)
z = Normal('z', mu=0.3 + phi*data[:-1], sigma=1, shape=len(data)-1)
ar_like = t['y'].logp({'z':data[1:], 'y': data})
reg_like = t['z'].logp({'z':data[1:], 'y': data})
diff --git a/pymc3/tests/test_plots.py b/pymc3/tests/test_plots.py
deleted file mode 100644
index fa5e9100c3..0000000000
--- a/pymc3/tests/test_plots.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import matplotlib
-matplotlib.use('Agg', warn=False) # noqa
-
-import numpy as np
-import pymc3 as pm
-from .checks import close_to
-
-from .models import multidimensional_model, simple_categorical
-from ..plots import traceplot, forestplot, autocorrplot, plot_posterior, energyplot, densityplot, pairplot
-from ..plots.utils import make_2d
-from ..step_methods import Slice, Metropolis
-from ..sampling import sample
-from ..tuning.scaling import find_hessian
-from .test_examples import build_disaster_model
-from pymc3.examples import arbitrary_stochastic as asmod
-import theano
-import pytest
-
-
-def test_plots():
- # Test single trace
- with asmod.build_model() as model:
- start = model.test_point
- h = find_hessian(start)
- step = Metropolis(model.vars, h)
- trace = sample(3000, tune=0, step=step, start=start, chains=1)
-
- traceplot(trace)
- forestplot(trace)
- plot_posterior(trace)
- autocorrplot(trace)
- energyplot(trace)
- densityplot(trace)
-
-def test_energyplot():
- with asmod.build_model():
- trace = sample(cores=1)
-
- energyplot(trace)
- energyplot(trace, shade=0.5, alpha=0)
- energyplot(trace, kind='hist')
-
-
-def test_plots_categorical():
- # Test single trace
- start, model, _ = simple_categorical()
- with asmod.build_model() as model:
- start = model.test_point
- h = find_hessian(start)
- step = Metropolis(model.vars, h)
- trace = sample(3000, tune=0, step=step, start=start, chains=1)
-
- traceplot(trace)
-
-
-def test_plots_multidimensional():
- # Test multiple trace
- start, model, _ = multidimensional_model()
- with model:
- h = np.diag(find_hessian(start))
- step = Metropolis(model.vars, h)
- trace = sample(3000, tune=0, step=step, start=start)
-
- traceplot(trace)
- plot_posterior(trace)
- forestplot(trace)
- densityplot(trace)
-
-
-@pytest.mark.xfail(condition=(theano.config.floatX == "float32"), reason="Fails on GPU due to cores=2")
-def test_multichain_plots():
- model = build_disaster_model()
- with model:
- # Run sampler
- step1 = Slice([model.early_mean_log__, model.late_mean_log__])
- step2 = Metropolis([model.switchpoint])
- start = {'early_mean': 2., 'late_mean': 3., 'switchpoint': 50}
- ptrace = sample(1000, tune=0, step=[step1, step2], start=start, cores=2)
-
- forestplot(ptrace, varnames=['early_mean', 'late_mean'])
- autocorrplot(ptrace, varnames=['switchpoint'])
- plot_posterior(ptrace)
-
-
-def test_make_2d():
- a = np.arange(4)
- close_to(make_2d(a), a[:, None], 0)
-
- n = 7
- a = np.arange(n * 4 * 5).reshape((n, 4, 5))
- res = make_2d(a)
-
- assert res.shape == (n, 20)
- close_to(a[:, 0, 0], res[:, 0], 0)
- close_to(a[:, 3, 2], res[:, 2 * 4 + 3], 0)
-
-
-def test_plots_transformed():
- with pm.Model():
- pm.Uniform('x', 0, 1)
- step = pm.Metropolis()
- trace = pm.sample(100, tune=0, step=step, chains=1)
-
- assert traceplot(trace).shape == (1, 2)
- assert traceplot(trace, plot_transformed=True).shape == (2, 2)
- assert autocorrplot(trace).shape == (1, 1)
- assert autocorrplot(trace, plot_transformed=True).shape == (2, 1)
- assert plot_posterior(trace).numCols == 1
- assert plot_posterior(trace, plot_transformed=True).shape == (2, )
-
- with pm.Model():
- pm.Uniform('x', 0, 1)
- step = pm.Metropolis()
- trace = pm.sample(100, tune=0, step=step, chains=2)
-
- assert traceplot(trace).shape == (1, 2)
- assert traceplot(trace, plot_transformed=True).shape == (2, 2)
- assert autocorrplot(trace).shape == (1, 2)
- assert autocorrplot(trace, plot_transformed=True).shape == (2, 2)
- assert plot_posterior(trace).numCols == 1
- assert plot_posterior(trace, plot_transformed=True).shape == (2, )
-
-def test_pairplot():
- with pm.Model() as model:
- a = pm.Normal('a', shape=2)
- c = pm.HalfNormal('c', shape=2)
- b = pm.Normal('b', a, c, shape=2)
- d = pm.Normal('d', 100, 1)
- trace = pm.sample(1000)
-
- pairplot(trace)
- pairplot(trace, hexbin=True, plot_transformed=True)
- pairplot(trace, sub_varnames=['a_0', 'c_0', 'b_1'])
-
\ No newline at end of file
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 73fc74ebac..be12a5a133 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,10 +1,10 @@
+arviz
bokeh>=0.12.13
CommonMark==0.5.4
graphviz>=0.8.3
h5py>=2.7.0
ipython
Keras>=2.0.8
-matplotlib>=1.5.0
nbsphinx>=0.2.13
nose>=1.3.7
nose-parameterized==0.6.0
diff --git a/setup.py b/setup.py
index 1e4ad13d2d..f6bb2b2e38 100755
--- a/setup.py
+++ b/setup.py
@@ -60,5 +60,8 @@ def get_version():
include_package_data=True,
classifiers=classifiers,
install_requires=install_reqs,
+ extras_require={
+ "plots": ["arviz"],
+ },
tests_require=test_reqs,
test_suite='nose.collector')