Merge branch 'master' into tf-1.14

language: python

python:

- '2.7'

- '3.5'

- '3.7'

sudo: required

dist: trusty

dist: xenial

install:

- wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O anaconda.sh;

- export python_version=$TRAVIS_PYTHON_VERSION

script:

- nosetests -q --with-flaky -a '!slow' --with-coverage --cover-package=deepchem

  -v deepchem --nologcapture

- if [ $TRAVIS_PYTHON_VERSION == '3.5' ]; then

- if [ $TRAVIS_PYTHON_VERSION == '3.7' ]; then

      find ./deepchem | grep .py$ |xargs python -m doctest -v;

  fi

- bash devtools/travis-ci/test_format_code.sh

### Soft Requirements

DeepChem has a number of "soft" requirements. These are packages which are needed for various submodules of DeepChem but not for the package as a whole.

* [rdkit](http://www.rdkit.org/docs/Install.html)

* [RDKit](http://www.rdkit.org/docs/Install.html)

* [six](https://pypi.python.org/pypi/six)

* [mdtraj](http://mdtraj.org/)

* [MDTraj](http://mdtraj.org/)

* [PDBFixer](https://github.com/pandegroup/pdbfixer)

* [Pillow](https://pillow.readthedocs.io/en/stable/)

### Super easy install via pip3

### Easy Install via Conda

```bash

conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem=2.1.0

conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem=2.2.0

```

**Note:** `Easy Install` installs the latest stable version of `deepchem` and _does not install from source_. If you need to install from source make sure you follow the steps [here](#using-a-conda-environment).

**Note:** The above commands install the latest stable version of `deepchem` and _do not install from source_. If you need to install from source make sure you follow the steps [here](#using-a-conda-environment).

### Using a Docker Image

Using a docker image requires an NVIDIA GPU.  If you do not have a GPU please follow the directions for [using a conda environment](#using-a-conda-environment)

## FAQ and Troubleshooting

1. ```deepchem``` currently supports both Python 2.7 and Python 3.5, and is supported on 64 bit Linux and Mac OSX. Note that DeepChem is not currently maintained for Python 3.6 or with other operating systems. 

1. DeepChem currently supports Python 3.5 through 3.7, and is supported on 64 bit Linux and Mac OSX. Note that DeepChem is not currently maintained for older versions of Python or with other operating systems.

2. Question: I'm seeing some failures in my test suite having to do with MKL

   ```Intel MKL FATAL ERROR: Cannot load libmkl_avx.so or libmkl_def.so.```

from deepchem.utils.genomics import encode_fasta_sequence

from deepchem.feat import UserDefinedFeaturizer

from deepchem.data import DiskDataset, NumpyDataset, ImageDataset

from scipy import misc

import zipfile

from PIL import Image

      _, extension = os.path.splitext(image_file)

      extension = extension.lower()

      if extension == ".png":

        image = misc.imread(image_file)

        image = np.array(Image.open(image_file))

        images.append(image)

      elif extension == ".tif":

        im = Image.open(image_file)

import unittest

import tempfile

from scipy import misc

from PIL import Image

import deepchem as dc

import zipfile

    self.data_dir = tempfile.mkdtemp()

    self.face = misc.face()

    self.face_path = os.path.join(self.data_dir, "face.png")

    misc.imsave(self.face_path, self.face)

    Image.fromarray(self.face).save(self.face_path)

    self.face_copy_path = os.path.join(self.data_dir, "face_copy.png")

    misc.imsave(self.face_copy_path, self.face)

    Image.fromarray(self.face).save(self.face_copy_path)

    # Create zip of image file

    #self.zip_path = "/home/rbharath/misc/cells.zip"

    # Create image directory

    self.image_dir = tempfile.mkdtemp()

    face_path = os.path.join(self.image_dir, "face.png")

    misc.imsave(face_path, self.face)

    Image.fromarray(self.face).save(face_path)

    face_copy_path = os.path.join(self.image_dir, "face_copy.png")

    misc.imsave(face_copy_path, self.face)

    Image.fromarray(self.face).save(face_copy_path)

  def test_png_simple_load(self):

    loader = dc.data.ImageLoader()

      # Compute atom properties

      atom_props = np.array([[atom.GetAtomicNum()] for atom in cmol.GetAtoms()])

      bond_props = bond_props.astype(np.float32)

      atom_props = atom_props.astype(np.float32)

    else:

      # Setup image

      img = np.zeros((self.img_size, self.img_size, 4))

          atom.GetHybridization().real,

      ] for atom in cmol.GetAtoms()])

      bond_props = bond_props.astype(np.float32)

      atom_props = atom_props.astype(np.float32)

      partial_charges = atom_props[:, 1]

      if np.any(np.isnan(partial_charges)):

        return []

    frac = np.linspace(0, 1, int(1 / self.res * 2))

    # Reshape done for proper broadcast

    frac = frac.reshape(-1, 1, 1)