Repository: jhamrick/plotchecker (language: python)

Issue:
error in alphas(), something wrong with _tile_or_trim()
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-73-c8dfe0c06f9f> in <module>()
      6 #pc.assert_y_data_equal(y)
      7 #pc.alphas
----> 8 pc.alphas
      9 
     10 

~/anaconda3/lib/python3.6/site-packages/plotchecker/scatterplot.py in alphas(self)
    147                 else:
    148                     alpha = np.array([x.get_alpha()])
--> 149                 all_alphas.append(self._tile_or_trim(points, alpha))
    150 
    151         return np.concatenate(all_alphas)

~/anaconda3/lib/python3.6/site-packages/plotchecker/base.py in _tile_or_trim(cls, x, y)
    129         if xn > yn:
    130             numrep = np.ceil(xn / yn)
--> 131             y = np.tile(y, (numrep,) + (1,) * (y.ndim - 1))
    132             yn = y.shape[0]
    133         if xn < yn:

~/anaconda3/lib/python3.6/site-packages/numpy/lib/shape_base.py in tile(A, reps)
    912                 c = c.reshape(-1, n).repeat(nrep, 0)
    913             n //= dim_in
--> 914     return c.reshape(shape_out)

TypeError: 'numpy.float64' object cannot be interpreted as an integer


Discussion / hints:

Fix patch:
diff --git a/.travis.yml b/.travis.yml
index 4ff8457..17b206a 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,6 +2,8 @@ language: python
 sudo: false
 python:
   - 3.4
+  - 3.5
+  - 3.6
 branches:
   only:
     - master
@@ -10,7 +12,9 @@ before_install:
   - chmod +x miniconda.sh
   - ./miniconda.sh -b
   - export PATH=/home/travis/miniconda3/bin:$PATH
-  - conda install --yes pip python==$TRAVIS_PYTHON_VERSION numpy matplotlib
+  - conda create --yes -n plotchecker python=$TRAVIS_PYTHON_VERSION
+  - source activate plotchecker
+  - conda install --yes numpy matplotlib
   - pip install flit
 install:
   - flit install --env -s
diff --git a/Examples.ipynb b/Examples.ipynb
index a364b78..d416846 100644
--- a/Examples.ipynb
+++ b/Examples.ipynb
@@ -10,9 +10,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%load_ext autoreload\n",
@@ -22,9 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -35,9 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from plotchecker import LinePlotChecker, ScatterPlotChecker, BarPlotChecker"
@@ -53,15 +47,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAEACAYAAABmjtVQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGo2J+H9IFZKBtl+NRwAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAD8CAYAAABNa2y4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ab59588>"
+       "<matplotlib.figure.Figure at 0x10ff53240>"
       ]
      },
      "metadata": {},
@@ -95,7 +87,6 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -133,15 +124,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAEACAYAAABbBguCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAD8CAYAAAB0FmJXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAHj5JREFUeJzt3X+MZWd93/H3l2sZ1FlCKryokX9g\n0y5VtlDNuCMPTZSGCuju0JVXalqwI9SiulgQHFWlikSE5aBdRQ2tSpuo7o9NavFDio3DH9XK2auV\nmoKQEJ540WwINjLaOhCvSesNEKQZRIxvv/3j3Ou9e3d+3Jm5P57n3vdLGt25Zw6z37m+H+73Oc9z\nzonMRJIkSZp3r5p2AZIkSVIJbIwlSZIkbIwlSZIkwMZYkiRJAmyMJUmSJMDGWJIkSQJsjCVJkiTA\nxliSJEkCbIwlSZIkAG6Y1j9800035e233z6tf14qzle/+tU/z8zD065jK+ZVulbJeQUzKw0aNrNT\na4xvv/12Lly4MK1/XipORHx72jVsx7xK1yo5r2BmpUHDZtalFJIkSRJDNMYR8UhEvBgRX9/m5xER\nvxURlyLiaxFx5+jLlCRJksZrmCPGnwKO7/DzVeBI9+t+4L8cvCxJkiRpsnZtjDPzS8D3dtjlJPCZ\nbDwJ/GRE/NSoCpS0N87ySPUwr1JZRrHG+Gbg+b7nl7vbrhMR90fEhYi4cOXKlRH805K28Cmc5ZFq\n8SnMq1SMiZ58l5lnMnM5M5cPHy72KjfF6XTgiSfg9OnmsdOZdkUqmbM8Uj3Mq1SWUVyu7QXg1r7n\nt3S3aQQ6HTh2DNbWYHMTFhZgZQXOn4dWa9rVqVLbzfL82XTKkbQD8ypN0CiOGJ8F/ml3HdTbgB9k\npoEdkXa7aYo3NiCzeVxba7ZL4+TSJ6kuZlY6uGEu1/Yo8BXgb0bE5Yi4LyI+GBEf7O5yDngOuAT8\nNvBLY6t2Dq2vN0eK+21uwsWL06lHM2GoWR6XPh2MS6A0IkPPyprZgzGzgiGWUmTmvbv8PIEPj6wi\nXWNpqVk+sbFxddvCAiwuTq8mVe8s8EBEPAas4CzPyLkESiNkXifAzKrHO98VbnW1CeehQxDRPK6s\nNNulrTjLM30ugdKwzGsZzKx6RnHyncao1WpGrO12s3xicbFpih3BajvO8kzfTkugTpyYTk0qk3kt\ng5lVj41xBVqtJpjjDGen0zTf6+vN8g2bb2n/XAIl1cXMqsfGWK6tkkastwRqMFMugZLKZGbVY2Os\na9ZWwbVrq5xCkvbOJVBSXcysemyM5doqaQzGuQTKpU/S6I172aK5rYONsVxbJVXEpU9SfcxtPbxc\nm7wknFQRLysl1cfc1sPGWK+srXr0UTh1qnl0FCuVybthSvUxt/VwKYWAyVwSTtLBufRJqo+5rYdH\njCWpIi59kupjbuvhEWNJqoiXlZLqY27rYWMsSZVx6ZNUH3NbB5dSSJIkSdgYS5IkSYCNsSRJkgTY\nGEuSJEmAjbEkSZIE2BhLkiRJgJdrk/as02muRbm+3tzNyGtRSpI0G2yMpT3odODYMVhba+5zv7DQ\n3L3o/HmbY0mSaudSCmkP2u2mKd7YgMzmcW2t2S5p+jodeOIJOH26eex0pl2RpL2YdoY9Yiztwfp6\nc6S43+Zmc4tP72YkTZczOlLdSsiwR4ylPVhaaoLab2Ghue+9pOlyRkeqWwkZtjGW9mB1tRm9HjoE\nEc3jykqzXdJ07TSjI6l8JWTYpRTSHrRazZROu90EdXHRq1JIpejN6GxsXN3mjI5UjxIybGMs7VGr\n1awndk2xVJbejM7g+kRndKQ6lJBhG2NJ0kxwRkeqWwkZtjGWJM0MZ3Skuk07w558J0mSJGFjLEmS\nJAE2xpIkSRJgYyxJkiQBQzbGEXE8Ip6NiEsR8dEtfn5bRHwhItYj4msR8e7RlypJkiSNz66NcUS0\ngIeBVeAocG9EHB3Y7UHg8cxcAu4B/vOoC5U0HAeyUl3MrFSOYY4Y3wVcysznMvMl4DHg5MA+CfxE\n9/vXAd8ZXYmShuVAVqqLmZXKMkxjfDPwfN/zy91t/T4OvC8iLgPngF/e6hdFxP0RcSEiLly5cmUf\n5UrahQNZqS5mVirIqE6+uxf4VGbeArwb+GxEXPe7M/NMZi5n5vLhw4dH9E9L6jOygaykiTCzUkGG\naYxfAG7te35Ld1u/+4DHATLzK8BrgJtGUaCkkRtqIOsMj1QMMytNyDCN8VPAkYi4IyJupFnfdHZg\nnz8F3gEQET9N0xibSmnyRjaQdYZHmggzKxVk18Y4M18GHgDOA9+gOQHg6Yg4FRF3d3f718AHIuKP\ngEeB92dmjqtoSdtyICvVxcxKBblhmJ0y8xzNuqb+bQ/1ff8M8LOjLU171elAuw3r67C0BKur0GpN\nuypNUma+HBG9gWwLeKQ3kAUuZOZZmoHsb0fEv6I5qceBrDQlZlYqy1CNscrX6cCxY7C2BpubsLAA\nKytw/rzN8bxxIFsPB7MCM1sbczvbbIwrtFUo2+2mKd7YaPbZ2Giet9tw4sR065V0PQezUn3M7eyz\nMa7MdqH8uZ9rnvfb3ISLF22MpRIMDmg7HQezUsk8CDWfbIwrs10of+Znmia5tx2a54uL06lT0lVb\nDWjf8AYHs1KpPAg1v0Z1gw9NyPr61qG84YYmtIcOQUTzuLLSjHAlTVf/gDazefzOd+DVr752Pwez\nUhm2yuzaWtMwLyxcu6+5nS0eMa7M0tLWR4bvvBM+9rEmzBcvNiH1hICteeKEJm2rAe2PfgRvehO8\n+OK1R6QczErTt9tBqMEjyeZ2dtgYV2Z1dftQtlrNVI7TOdsb9YkTNtkaxlYD2kOH4JOfbN4vDmav\nZa40bR6E2r/a82tjXJlWq2niDOX+jPLECc9O1rC2G9CeOHF1QKvGKHNV+we0pseDUPszCwefbIwr\nZCj3b7vpsf2cOOHZyRqWA9rhjSpXDlx1EGZ2f2bh4JMn32mu9KbH+u33xImdmmxpUG9A++CDV48U\n63qjytV2J0+126OrVbPNzO7dKD8Xp5VhG+MddDrwxBNw+nTz2OlMuyIdVG96bBRX7xhlky2pMapc\nOXCVJm8WDj65lGIbTsPNplFOj+20Bk3T5/rSOo0qV9udPOXAtUzmdTaM8nNxWhm2Md6G60dn16jW\naLsGrVwObOs1qlw5cK2HeZ0ds3DwycZ4G6M8SUuzyxMhy+TAtm6jyJUD13qY19lS+8EnG+NtOA0n\n1cuBrcCBay3Mq7YzjQx78t02RnmSlqTJ8sRIqR7mVSXxiPE2nIaT6uX6Uqke5lUlsTHegdNwUp0c\n2Er1MK8qiY2xpJnkwFaqh3lVKVxjLEmSJGFjLEmSJAE2xpIkSRJgYyxJkiQBNsaSJEkSYGMsSZIk\nATbGkiRJEmBjLEmSJAE2xpIkSRJgYyxJkiQBNsaSJEkSYGMsSZIkATbGkiRJEmBjLEmSJAFDNsYR\ncTwino2ISxHx0W32eU9EPBMRT0fE7462TEnDMq9SPcyrVJYbdtshIlrAw8C7gMvAUxFxNjOf6dvn\nCPCrwM9m5vcj4g3jKljS9syrVA/zKpVnmCPGdwGXMvO5zHwJeAw4ObDPB4CHM/P7AJn54mjLlDQk\n8yrVw7xKhRmmMb4ZeL7v+eXutn5vBt4cEV+OiCcj4vhWvygi7o+ICxFx4cqVK/urWNJORpZXSWNn\nXqXC7LqUYg+/5wjwduAW4EsR8dbM/Iv+nTLzDHAGYHl5OUf0b0vF6nSg3Yb1dVhagtVVaLWmXdVw\neY2I+4H7AW677bZJ1yiNXaH5HDRUXsHMSqMwTGP8AnBr3/Nbutv6XQbWMvPHwJ9ExDdpgvzUSKqU\nKtTpwLFjsLYGm5uwsAArK3D+/Fg/fEeWVweymmVTyuegkX6+mlnNm3EMbodZSvEUcCQi7oiIG4F7\ngLMD+/wPmtEsEXETzdTPcwcrTapbu9186G5sQGbzuLbWbB8j8yoNYUr5HGRepX3qDW7vvRd+7dea\nx2PHmu0HsWtjnJkvAw8A54FvAI9n5tMRcSoi7u7udh74bkQ8A3wB+JXM/O7BSpPqtr7eHInqt7kJ\nFy+O7980r9JwppHPQeZV2r9xDW6HWmOcmeeAcwPbHur7PoGPdL8k0UzrLCw0Ye1ZWIDFxfH+u+ZV\n2t208jnIvEr7s9Pg9sSJ/f9e73wnjcnqarNm8dAhiGgeV1aa7ZKmy3xKdesNbvuNYnA7qqtSSBrQ\najUn8rTbzQh2cbHYs96luWM+pbr1BreDJ9AedHBrYyyNUavVTOkcZFpH0niYT6le4xrc2hhLkiSp\nOuMY3LrGWJIkScLGWJIkSQJsjCVJkiTAxliSJEkCbIwlSZIkwMZYkiRJAmyMJUmSJMDGWJIkSQJs\njCVJkiTAxliSJEkCbIwlSZIkwMZYkiRJAmyMJUmSJMDGWJIkSQJsjCVJkiTAxliSJEkCbIwlSZIk\nwMZYkiRJAmyMJUmSJMDGWJIkSQLghmkXIO1FpwPtNqyvw9ISrK5CqzXtqiT1M6dSveY9vzbGqkan\nA8eOwdoabG7CwgKsrMD58/MVWqlk5lSql/l1KYUq0m43Yd3YgMzmcW2t2S6pDOZUqpf5tTFWRdbX\nmxFsv81NuHhxOvVIup45leplfm2MVZGlpWZap9/CAiwuTqceSdczp1K9zK+NsSqyutqsdTp0CCKa\nx5WVZrukMphTqV7m15PvVJFWqzkBoN1upnUWF+fvbFmpdOZUqpf5tTFWZVotOHGi+ZJUJnMq1Wve\n8+tSCmnGRMTxiHg2Ii5FxEd32O8XIiIjYnmS9Um6lpmVyjFUY2xopTpERAt4GFgFjgL3RsTRLfZ7\nLfAvgbXJViipn5mVyrJrY2xoparcBVzKzOcy8yXgMeDkFvudBj4B/GiSxUm6jpmVCjLMEWNDK9Xj\nZuD5vueXu9teERF3Ardm5u9PsjBJWzKzUkGGOfluq9Cu9O/QH9qI+JUR1icNbd7v7z6MiHgV8Eng\n/UPsez9wP8Btt9023sI0F8zo3plZlWJe8nvgq1IYWpXA+7u/4gXg1r7nt3S39bwWeAvwxYgA+GvA\n2Yi4OzMv9P+izDwDnAFYXl7OcRat2WdGt2VmVbx5yu8wSyn2EtpvAW+jCe11J+Bl5pnMXM7M5cOH\nD++/ammA93d/xVPAkYi4IyJuBO4BzvZ+mJk/yMybMvP2zLwdeBK47gNWGjUzui0zq+LNU36HaYwN\nrYrn/d0bmfky8ABwHvgG8HhmPh0RpyLi7ulWp3lmRrdmZlWDecrvrkspMvPliOiFtgU80gstcCEz\nz+78G6Tx693ffWPj6rZ5u797T2aeA84NbHtom33fPomaJDO6PTOr0s1Tfoe6jnFmnsvMN2fmX8/M\nX+9ue2irpjgz3+7RYk2a93eXymZGpXrNU369JbRmgvd3l8pmRqV6zVN+bYw1M+b9/u5S6cyoVK95\nye9QSykkSZKkWWdjLEmSJGFjLEmSJAE2xpIkSRJgYyxJkiQBNsaSJEkSYGMsSZIkATbGkiRJEuAN\nPiSNQafT3CFpfR2Wlmb3Dkmqk+/Pq3wtVKNxvm+LbIwNqmo3z+/hTgeOHYO1NdjchIUFWFlpbic6\nL6+ByuX78ypfC9Vo3O/b4hpjg6razft7uN1u/vaNjeb5xkbzvN2e/VuJqny+P6/ytVCNxv2+LW6N\ncf8fnHntHyzVYN7fw+vrzYCg3+YmXLw4nXqkfr4/r/K1UI3G/b4trjE2qKrdvL+Hl5aao+T9FhZg\ncXE69Uj9fH9e5WuhGo37fVtcY2xQVbt5fw+vrjZLRw4dgojmcWWl2S5Nm+/Pq3wtVKNxv2+LW2Pc\n+4MH12caVNVi3t/DrVaznrrdbo6SLy7O18mHKpvvz6t8LVSjcb9vIzNH85v2aHl5OS9cuLDlz3pn\n9BtU1Wo/7+GI+GpmLk+mwr3ZKa+6ap6vRjJvSs4rmNm9MLfzYdjMFnfEGJo35IkTnhVrWOvle3j+\nzPvVSKQamVsNKrIxlmGVauOlr6T6mFsNKu7kOzXm/ZJfUm3m/WokUo3MrQbZGBfKsEp1mferkUg1\nMrcaZGNcKMMq1cVLX0n1Mbca5BrjQs37Jb+k2njpK6k+5laDbIwLZVil+ng1Eqk+5lb9bIwLZlil\nsnlJRak+5lY7sTEuhEGV6uIlFaX6mFvtxsa4AAZVqo/XP5XqY261G69KUQCvWSzVx0sqSvUxt9qN\njXEBDKpUHy+pKNXH3Go3NsYFMKhSfbz+qVQfc6vduMa4AF6zWKMUEceB3wRawO9k5m8M/PwjwL8A\nXgauAP88M7898UIr5yUVNQrmdbLMrXZjY1wAg6pRiYgW8DDwLuAy8FREnM3MZ/p2WweWM/OHEfEh\n4N8C7518tfXzkoo6CPM6HeZWOxlqKUVEHI+IZyPiUkR8dIuffyQinomIr0XEH0TEG0df6mzrBfXB\nB5tHm2Lt013Apcx8LjNfAh4DTvbvkJlfyMwfdp8+Cdwy4RolNcyrVJhdG+O+Ee0qcBS4NyKODuzW\nG9H+beDzNCNaSZN3M/B83/PL3W3buQ/w+ifSdJhXqTDDHDF2RCvNoIh4H7AM/Lttfn5/RFyIiAtX\nrlyZbHGSrrFbXrv7mFnpgIZpjB3RSvV4Abi17/kt3W3XiIh3Ah8D7s7Mv9zqF2Xmmcxczszlw4cP\nj6VYac6NLK9gZqVRGOnl2jwCJU3dU8CRiLgjIm4E7gHO9u8QEUvAf6P5kH1xCjVKaphXqTDDXJVi\nryPan9/pCBRwBmB5eTn3XK2kHWXmyxHxAHCe5vJPj2Tm0xFxCriQmWdpBq6HgN+LCIA/zcy7p1a0\nZl6n01x1Z329uW67V91pmFfVYN7yO0xj/MqIlqYhvgf4xf4d+ka0xx3RStOVmeeAcwPbHur7/p0T\nL0pzq9OBY8euv077+fOz/eE6LPOqks1jfnddSpGZLwO9Ee03gMd7I9qI6I1a+0e0FyPi7Da/Thqp\nTgeeeAJOn24eO51pVySpX7vdfKhubEBm87i21myXVLZ5zO9QN/hwRKsSzeNIVqrN+nqTz36bm83N\njLzBglS2eczvSE++kyZpHkeyUm2WlppBa7+FheYOn5LKNo/5tTFWtXYayUoqw+pqM5Nz6BBENI8r\nK812SWWbx/wOtZRCKlFvJLuxcXXbrI9kpdq0Ws3ypna7GbQuLs7+We3SrJjH/NoYq1q9kezgGuNZ\nHslKNWq1mvWIs7omUZpl85ZfG2NVax5HspIkaXxsjFW1eRvJSpKk8fHkO0mSJAkbY0mSJAmwMZYk\nSZIAG2NJkiQJsDGWJEmSABtjSZIkCfBybZKkOdXpNNdBX19v7qTpddAl2RhLkuZOpwPHjl1/58zz\n522OpVqMY3BrYyxJmjvtdtMUb2w0zzc2mufttjcMkmowrsGta4ylMep04Ikn4PTp5rHTmXZFkqA5\nwrS5ee22zc3m9vKSytc/uM28dnB7EB4xlsbEqVqpXEtLTSZ7R4yheb64OL2aJA1vp8HtQWZ9PGIs\njcm4RrOSDm51tRmoHjoEEc3jykqzXVL5eoPbfqMY3HrEWBqTcY1mJR1cq9XM3rTbTSYXF70qhVST\n3uB2cFb2oINbG2NpTJyqVWm8PNm1Wq1mkOpAVaUzu9cb1+DWxlgak3GNZqX9cM27VCezu71xDG5t\njHVgjmS35lStSuLlyaQ6md3JsjHWgTiS3ZlTtRqXvQ5IXfMu1cnsTpaNsQ7EkaxqVutsx34GpK55\n1yyoNbM9+6nf7E6WjbEOxJGsalXzbMd+BqSueVftas4s7L9+sztZNsYjUvsoFhzJar7UPNuxnwGp\na95Vu5ozC/uv3+xOlo3xCNQ+igVHspo/Nc927HdA6pp31azmzMLB6je7k+Od70ZgFu5wtt+/oTeS\nffRROHWqeaxpQKD5Na67Jk2Cd23TPKo5s1B//fPCxngEdhoF1uIgf0NvJPvgg82jTbFqUHNz6YBU\n86jmzEL99c8Ll1KMwCyss52Fv0Hai9rX7Tm1qnkzC5mtuf55YWM8ArOwznYW/gY1IuI48JtAC/id\nzPyNgZ+/GvgM8HeA7wLvzcxvTbrOEthcSnWpPbO11z8PbIxHYBZGgbPwNwgiogU8DLwLuAw8FRFn\nM/OZvt3uA76fmX8jIu4BPgG8d/LVSgIHs1JJbIxHZBZGgbPwN4i7gEuZ+RxARDwGnAT6G+OTwMe7\n338e+E8REZmZkyxUkoNZqTRDnXwXEccj4tmIuBQRH93i56+OiM91f74WEbePulBJQ7kZeL7v+eXu\nti33ycyXgR8Ar59IdZIGvTKYzcyXgN5gtt9J4NPd7z8PvCMiYoI1SnNj18a4bzS7ChwF7o2IowO7\nvTKaBf4DzWhWUsUi4v6IuBARF65cuTLtcqRZNbLBrJmVDm6YI8aOZqV6vADc2vf8lu62LfeJiBuA\n19GsW7xGZp7JzOXMXD58+PCYypU0KmZWOrhhGmOnZqV6PAUciYg7IuJG4B7g7MA+Z4F/1v3+HwP/\ny/XF0tSMbDAr6eAmeoMPp3mk8eoOTB8AzgPfAB7PzKcj4lRE3N3d7b8Dr4+IS8BHgOvOG5A0MQ5m\npYLEbtmKiL8LfDwzj3Wf/ypAZv6bvn3Od/f5Snc0+3+AwzsFNyKuAN/e4kc3AX++1z9kAkqtC6xt\nv0qr7Y2ZWeT85w553UpprytY0zBKqwfKrmlkeY2IdwP/keZybY9k5q9HxCngQmaejYjXAJ8FloDv\nAff0rjyzw+8cJrMlvr5gXXtVal1QVm1DZXaYxvgG4JvAO2imc54CfjEzn+7b58PAWzPzg91Lyfyj\nzHzPfqqOiAuZubyf/+04lVoXWNt+lVxbzUp8Xa1pd6XVA9Y0bqX+Lda1N6XWBWXXtp1dr2OcmS9H\nRG9qtjeafbp/NEszNfvZ7tTs92imgiRJkqRqDHWDj8w8B5wb2PZQ3/c/Av7JaEuTJEmSJmeiJ98N\n6cy0C9hGqXWBte1XybXVrMTX1Zp2V1o9YE3jVurfYl17U2pdUHZtW9p1jbEkSZI0D0o8YixJkiRN\n3FQa44g4HhHPRsSliLjuGqoR8eqI+Fz352sRcXtBtX0kIp6JiK9FxB9ExBtLqa1vv1+IiIyIiZ0J\nOkxtEfGe7mv3dET8bim1RcRtEfGFiFjv/nd996Rqq1WJGS4xuyVmtsSslpjRiHgkIl6MiK9v8/OI\niN/q1vy1iLhz3DXtV4l5HbKuqXzelpjbYeuaxudsifk9kMyc6BfNlS3+N/Am4Ebgj4CjA/v8EvBf\nu9/fA3yuoNr+PvBXut9/qKTauvu9FvgS8CSwXEptwBFgHfir3edvKKi2M8CHut8fBb41idpq/Sox\nwyVmt8TMlpjVUjMK/D3gTuDr2/z83UAbCOBtwNq4axrj6zvxz9wSMztsXd39JvpZW2J291BXVZ+x\n0zhifBdwKTOfy8yXgMeAkwP7nAQ+3f3+88A7IiJKqC0zv5CZP+w+fZLm9p2TMMzrBnAa+ATwownV\nNWxtHwAezszvA2TmiwXVlsBPdL9/HfCdCdVWqxIzXGJ2S8xsiVktMqOZ+SWay49u5yTwmWw8Cfxk\nRPzUuOvahxLzOlRdU/q8LTG3w9Y1jc/ZIvN7ENNojG8Gnu97frm7bct9srnF7Q+A1xdSW7/7aI4Y\nTMKutXWn8m7NzN+fUE09w7xubwbeHBFfjognI+J4QbV9HHhfRFymuSzhL0+mtGqVmOESs1tiZkvM\naq0Z3et7blpKzOuwdfWb1Odtibkdqi6m8zlba363NdR1jHW9iHgfsAz8/LRrAYiIVwGfBN4/5VK2\ncwPNNM/baUb9X4qIt2bmX0y1qsa9wKcy899Hcwv0z0bEWzLz/027MI1eKdktOLMlZtWMzrFSMtut\npdTcQpnZhcryO40jxi8At/Y9v6W7bct9orkl9euA7xZSGxHxTuBjwN2Z+ZcTqGuY2l4LvAX4YkR8\ni2bN29kJnRQwzOt2GTibmT/OzD+huc34kUJquw94HCAzvwK8hub+7tpaiRkuMbslZrbErNaa0aHe\ncwUoMa/D1jWNz9sScztMXTCdz9la87u9SS9qphnRPAfcwdWF2n9rYJ8Pc+2JAI8XVNsSzULzI6W9\nbgP7f5HJnXw3zOt2HPh09/ubaKZeXl9IbW3g/d3vf5pm/VNM8r9vTV8lZrjE7JaY2RKzWnJGgdvZ\n/uS7f8i1J9/94STeV2N6fSf+mVtiZoeta2D/sed2D6/XxD9nS87vvv+mqfyjzdm83+y+4T/W3XaK\nZkQIzWji94BLwB8Cbyqotv8J/F/gYvfrbCm1Dew7kbDu4XULmumnZ4A/Bu4pqLajwJe7gb4I/INJ\n1VbrV4kZLjG7JWa2xKyWmFHgUeDPgB/THIm7D/gg8MG+1+nhbs1/PMn/vx3D6zuVz9wSMztMXQP7\nTiS3Q75eU/mcLTG/B/nyzneSJEkS3vlOkiRJAmyMJUmSJMDGWJIkSQJsjCVJkiTAxliSJEkCbIwl\nSZIkwMZYkiRJAmyMJUmSJAD+P5XwvJlLSYWdAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ac709b0>"
+       "<matplotlib.figure.Figure at 0x1101d85f8>"
       ]
      },
      "metadata": {},
@@ -159,11 +148,11 @@
     "axes[0].plot(x, y, 'o', color='b', ms=5)\n",
     "\n",
     "# create a scatter plot with scatter\n",
-    "axes[1].scatter(x, y, s=25, linewidth=0.5)\n",
+    "axes[1].scatter(x, y, s=25, c='b')\n",
     "\n",
     "# create a scatter plot with plot *and* scatter!\n",
     "axes[2].plot(x[:10], y[:10], 'o', color='b', ms=5)\n",
-    "axes[2].scatter(x[10:], y[10:], s=25, linewidth=0.5)\n",
+    "axes[2].scatter(x[10:], y[10:], s=25, c='b')\n",
     "\n",
     "fig.set_size_inches(12, 4)"
    ]
@@ -171,9 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -190,8 +177,8 @@
     "    pc.assert_x_data_equal(x)\n",
     "    pc.assert_y_data_equal(y)\n",
     "    pc.assert_colors_equal('b')\n",
-    "    pc.assert_edgecolors_equal('k')\n",
-    "    pc.assert_edgewidths_equal(0.5)\n",
+    "    pc.assert_edgecolors_equal('b')\n",
+    "    pc.assert_edgewidths_equal(1)\n",
     "    pc.assert_sizes_equal(25)\n",
     "    pc.assert_markersizes_equal(5)\n",
     "    pc.assert_alphas_equal(1.0)\n",
@@ -206,15 +193,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAEACAYAAABbBguCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAD8CAYAAAB0FmJXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10adf7438>"
+       "<matplotlib.figure.Figure at 0x110307ba8>"
       ]
      },
      "metadata": {},
@@ -237,11 +222,11 @@
     "    axes[0].plot(x[i], y[i], 'o', color=colors[i], ms=sizes[i])\n",
     "\n",
     "# create a scatter plot with scatter\n",
-    "axes[1].scatter(x, y, c=colors, s=sizes ** 2, linewidth=0.5)\n",
+    "axes[1].scatter(x, y, c=colors, s=sizes ** 2)\n",
     "\n",
     "# create a scatter plot with scatter, using a loop\n",
     "for i in range(20):\n",
-    "    axes[2].scatter(x[i], y[i], c=colors[i], s=sizes[i] ** 2, linewidth=0.5)\n",
+    "    axes[2].scatter(x[i], y[i], c=colors[i], s=sizes[i] ** 2)\n",
     "\n",
     "fig.set_size_inches(12, 4)"
    ]
@@ -249,9 +234,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -268,9 +251,9 @@
     "    pc.assert_x_data_equal(x)\n",
     "    pc.assert_y_data_equal(y)\n",
     "    pc.assert_colors_equal(colors)\n",
-    "    pc.assert_edgecolors_equal('k')\n",
-    "    pc.assert_edgewidths_equal(0.5)\n",
-    "    pc.assert_sizes_equal(sizes ** 2)\n",
+    "    pc.assert_edgecolors_equal(colors)\n",
+    "    pc.assert_edgewidths_equal(1)\n",
+    "    pc.assert_sizes_allclose(sizes ** 2)\n",
     "    pc.assert_markersizes_equal(sizes)\n",
     "    pc.assert_alphas_equal(colors[:, 3])\n",
     "    \n",
@@ -291,15 +274,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAEACAYAAABiewVtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAD8CAYAAABNa2y4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nypDSYum+AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10b0efc18>"
+       "<matplotlib.figure.Figure at 0x1105cdb70>"
       ]
      },
      "metadata": {},
@@ -324,7 +305,7 @@
     "idx = np.arange(len(x))\n",
     "np.random.shuffle(idx)\n",
     "for i in idx:\n",
-    "    axes[1].bar(x[i] - (widths[i] / 2), y[i], width=widths[i], color=colors[i, :3], alpha=alphas[i])\n",
+    "    axes[1].bar(x[i] - (widths[i] / 2), y[i], align='edge', width=widths[i], color=colors[i, :3], alpha=alphas[i])\n",
     "    \n",
     "fig.set_size_inches(8, 4)"
    ]
@@ -332,9 +313,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -377,9 +356,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/flit.ini b/flit.ini
index 3ca84c7..054cf07 100644
--- a/flit.ini
+++ b/flit.ini
@@ -2,7 +2,7 @@
 module = plotchecker
 author = Jessica B. Hamrick
 author-email = jhamrick@berkeley.edu
-requires = matplotlib
+requires = matplotlib>=2
     numpy
     six
 dev-requires = pytest
diff --git a/plotchecker/base.py b/plotchecker/base.py
index b782a6f..b2ca845 100644
--- a/plotchecker/base.py
+++ b/plotchecker/base.py
@@ -127,7 +127,7 @@ def _tile_or_trim(cls, x, y):
         xn = x.shape[0]
         yn = y.shape[0]
         if xn > yn:
-            numrep = np.ceil(xn / yn)
+            numrep = int(np.ceil(xn / yn))
             y = np.tile(y, (numrep,) + (1,) * (y.ndim - 1))
             yn = y.shape[0]
         if xn < yn:


Test patch:
diff --git a/plotchecker/tests/test_barplot.py b/plotchecker/tests/test_barplot.py
index 520c262..e3094a8 100644
--- a/plotchecker/tests/test_barplot.py
+++ b/plotchecker/tests/test_barplot.py
@@ -28,7 +28,7 @@ def test_data_left(axis):
     x = np.arange(10)
     y = np.linspace(1, 5, 10)
     b = np.linspace(0, 1, 10)
-    axis.bar(x, y, bottom=b)
+    axis.bar(x, y, bottom=b, align='edge')
 
     pc = BarPlotChecker(axis)
     pc.assert_centers_equal(x + 0.4)
@@ -42,7 +42,7 @@ def test_data_left_allclose(axis):
     x = np.arange(1, 11)
     y = np.linspace(1, 5, 10)
     b = np.linspace(0.1, 1, 10)
-    axis.bar(x + err, y + err, bottom=b + err)
+    axis.bar(x + err, y + err, bottom=b + err, align='edge')
 
     pc = BarPlotChecker(axis)
 
@@ -223,7 +223,7 @@ def test_alphas_allclose(axis):
     err = 1e-12
     x = np.arange(5)
     y = np.linspace(1, 5, 5)
-    alphas = np.linspace(0.1, 1, 5)
+    alphas = np.linspace(0.1, 0.99, 5)
 
     for i in range(len(x)):
         axis.bar(x[i], y[i], alpha=alphas[i] + err, align='center')
@@ -283,7 +283,7 @@ def test_example(axes):
     idx = np.arange(len(x))
     np.random.shuffle(idx)
     for i in idx:
-        axes[1].bar(x[i] - (widths[i] / 2), y[i], width=widths[i], color=colors[i, :3], alpha=alphas[i])
+        axes[1].bar(x[i] - (widths[i] / 2), y[i], width=widths[i], align='edge', color=colors[i, :3], alpha=alphas[i])
 
     # plot them in yet another order
     idx = np.arange(len(x))[::-1]
diff --git a/plotchecker/tests/test_lineplot.py b/plotchecker/tests/test_lineplot.py
index 23b9800..efb1032 100644
--- a/plotchecker/tests/test_lineplot.py
+++ b/plotchecker/tests/test_lineplot.py
@@ -134,7 +134,7 @@ def test_colors_allclose(axis):
     """Are the colors almost correct?"""
     err = 1e-12
     color = np.array([0.1, 1, 1])
-    axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], color=color + err)
+    axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], color=color - err)
 
     pc = LinePlotChecker(axis)
     with pytest.raises(AssertionError):
@@ -217,7 +217,7 @@ def test_markerfacecolors_allclose(axis):
     """Are the markerfacecolors almost correct?"""
     err = 1e-12
     markerfacecolor = np.array([0.1, 1, 1])
-    axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], markerfacecolor=markerfacecolor + err)
+    axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], markerfacecolor=list(markerfacecolor + err))
 
     pc = LinePlotChecker(axis)
     with pytest.raises(AssertionError):
@@ -229,44 +229,44 @@ def test_markerfacecolors_allclose(axis):
 
 def test_markeredgecolors(axis):
     """Are the marker edge colors correct?"""
-    # inherit the color from the line -- this should actually be the default (black)
+    # inherit the color from the line -- this should actually be the default (grey)
     axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], marker='o', color='c')
     pc = LinePlotChecker(axis)
-    pc.assert_markeredgecolors_equal(['k'])
+    pc.assert_markeredgecolors_equal([[0, 0.75, 0.75]])
 
     # add another line, using rgb
     axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], marker='o', markeredgecolor=[0, 1, 1])
     pc = LinePlotChecker(axis)
-    pc.assert_markeredgecolors_equal(['k', [0, 1, 1]])
+    pc.assert_markeredgecolors_equal([[0, 0.75, 0.75], [0, 1, 1]])
 
     # add another line, using hex values
     axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], marker='o', markeredgecolor='#FF0000')
     pc = LinePlotChecker(axis)
-    pc.assert_markeredgecolors_equal(['k', [0, 1, 1], '#FF0000'])
+    pc.assert_markeredgecolors_equal([[0, 0.75, 0.75], [0, 1, 1], '#FF0000'])
 
     # add another line, using matplotlib colors
     axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], marker='o', markeredgecolor='g')
     pc = LinePlotChecker(axis)
-    pc.assert_markeredgecolors_equal(['k', [0, 1, 1], '#FF0000', 'g'])
+    pc.assert_markeredgecolors_equal([[0, 0.75, 0.75], [0, 1, 1], '#FF0000', 'g'])
 
     # add another line, using full matplotlib color names
     axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], marker='o', markeredgecolor='magenta')
     pc = LinePlotChecker(axis)
-    pc.assert_markeredgecolors_equal(['k', [0, 1, 1], '#FF0000', 'g', 'magenta'])
+    pc.assert_markeredgecolors_equal([[0, 0.75, 0.75], [0, 1, 1], '#FF0000', 'g', 'magenta'])
 
     # and now two more lines, plotted at the same time
     x = np.array([[4.3, 5, 6], [5.3, 6, 7]])
     y = np.array([[3.4, 4, 7], [10.2, 9, 8]])
     axis.plot(x.T, y.T, marker='o', markeredgecolor='k')
     pc = LinePlotChecker(axis)
-    pc.assert_markeredgecolors_equal(['k', [0, 1, 1], '#FF0000', 'g', 'magenta', 'k', 'k'])
+    pc.assert_markeredgecolors_equal([[0, 0.75, 0.75], [0, 1, 1], '#FF0000', 'g', 'magenta', 'k', 'k'])
 
 
 def test_markeredgecolors_allclose(axis):
     """Are the markeredgecolors almost correct?"""
     err = 1e-12
     markeredgecolor = np.array([0.1, 1, 1])
-    axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], markeredgecolor=markeredgecolor + err)
+    axis.plot([1, 2.17, 3.3, 4], [2.5, 3.25, 4.4, 5], markeredgecolor=list(markeredgecolor + err))
 
     pc = LinePlotChecker(axis)
     with pytest.raises(AssertionError):
diff --git a/plotchecker/tests/test_scatterplot.py b/plotchecker/tests/test_scatterplot.py
index 4931db8..bfac12e 100644
--- a/plotchecker/tests/test_scatterplot.py
+++ b/plotchecker/tests/test_scatterplot.py
@@ -217,9 +217,9 @@ def test_edgecolors(axis):
     c = np.random.rand(10, 3)
 
     for i in range(5):
-        axis.plot(x[i], y[i], 'o', markeredgecolor=c[i])
+        axis.plot(x[i], y[i], 'o', markeredgecolor=list(c[i]))
     for i in range(5, 10):
-        axis.scatter(x[i], y[i], edgecolor=c[i])
+        axis.scatter(x[i], y[i], edgecolor=list(c[i]))
 
     pc = ScatterPlotChecker(axis)
     pc.assert_edgecolors_equal(c)
@@ -233,9 +233,9 @@ def test_edgecolors_allclose(axis):
     c[c < 1e-3] = 1e-3
 
     for i in range(5):
-        axis.plot(x[i], y[i], 'o', markeredgecolor=c[i] + err)
+        axis.plot(x[i], y[i], 'o', markeredgecolor=list(c[i] + err))
     for i in range(5, 10):
-        axis.scatter(x[i], y[i], edgecolor=c[i] + err)
+        axis.scatter(x[i], y[i], edgecolor=list(c[i] + err))
 
     pc = ScatterPlotChecker(axis)
     with pytest.raises(AssertionError):
@@ -375,19 +375,19 @@ def test_example_1(axes):
     axes[0].plot(x, y, 'o', color='b', ms=5, alpha=0.8)
 
     # create a scatter plot with scatter
-    axes[1].scatter(x, y, s=25, linewidth=0.5, alpha=0.8)
+    axes[1].scatter(x, y, s=25, c='b', alpha=0.8)
 
     # create a scatter plot with plot *and* scatter!
     axes[2].plot(x[:10], y[:10], 'o', color='b', ms=5, alpha=0.8)
-    axes[2].scatter(x[10:], y[10:], s=25, linewidth=0.5, alpha=0.8)
+    axes[2].scatter(x[10:], y[10:], s=25, c='b', alpha=0.8)
 
     for ax in axes:
         pc = ScatterPlotChecker(ax)
         pc.assert_x_data_equal(x)
         pc.assert_y_data_equal(y)
         pc.assert_colors_equal('b')
-        pc.assert_edgecolors_equal('k')
-        pc.assert_edgewidths_equal(0.5)
+        pc.assert_edgecolors_equal('b')
+        pc.assert_edgewidths_equal(1)
         pc.assert_sizes_equal(25)
         pc.assert_markersizes_equal(5)
         pc.assert_alphas_equal(0.8)
@@ -406,19 +406,19 @@ def test_example_2(axes):
         axes[0].plot(x[i], y[i], 'o', color=colors[i], ms=sizes[i])
 
     # create a scatter plot with scatter
-    axes[1].scatter(x, y, c=colors, s=sizes ** 2, linewidth=0.5)
+    axes[1].scatter(x, y, c=colors, s=sizes ** 2)
 
     # create a scatter plot with scatter, using a loop
     for i in range(20):
-        axes[2].scatter(x[i], y[i], c=colors[i], s=sizes[i] ** 2, linewidth=0.5)
+        axes[2].scatter(x[i], y[i], c=colors[i], s=sizes[i] ** 2)
 
     for ax in axes:
         pc = ScatterPlotChecker(ax)
         pc.assert_x_data_equal(x)
         pc.assert_y_data_equal(y)
         pc.assert_colors_equal(colors)
-        pc.assert_edgecolors_equal('k')
-        pc.assert_edgewidths_equal(0.5)
+        pc.assert_edgecolors_equal(colors)
+        pc.assert_edgewidths_equal(1)
         pc.assert_sizes_equal(sizes ** 2)
         pc.assert_markersizes_equal(sizes)
         pc.assert_alphas_equal(colors[:, 3])