Fix installation process #18 (#19)

* Fix installation issues (part 1) - #18

* Update script entrypoint and CI pipeline - #18

* Fix CI workflow

* Fix CI workflow

* Fix possible python versions - #18

.github/workflows/main.yml

@@ -6,7 +6,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest]
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.7, 3.8]
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python
@@ -15,8 +15,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python -m pip install --upgrade pip setuptools wheel twine pytest pytest-cov
-        pip install -r requirements.txt
+        pip install .
     - name: Set environment
       run: |
               echo "PYTHONPATH=/home/runner/work/DeepCoil/DeepCoil" >> $GITHUB_ENV

CHANGELOG.md

@@ -1,5 +1,8 @@
 ## Changelog
 
+## v2.0.2
+- Fixed installation issues.
+
 ### v2.0.1
 - Updated DC2 neural network weights.
 

README.md

@@ -15,7 +15,7 @@
 **Older DeepCoil versions are available [here](https://github.com/labstructbioinf/DeepCoil/releases).**
 
 ### **Requirements and installation** ###
-DeepCoil requires `python>=3.6.1` and `pip>=19.0`. Other requirements are specified in the `requirements.txt` file.
+DeepCoil requires `python>=3.7,<3.9` and `pip>=19.0`.
 
 The most convenient way to install **DeepCoil** is to use pip:
 ```bash

deepcoil/run_deepcoil.py

@@ -0,0 +1,85 @@
+import os
+import argparse
+from Bio import SeqIO
+from deepcoil import DeepCoil
+from deepcoil.utils import is_fasta, sharpen_preds, plot_preds
+
+
+def main():
+
+    parser = argparse.ArgumentParser(description='DeepCoil')
+    parser.add_argument('-i',
+                        help='Input file with sequence in fasta format.',
+                        required=True,
+                        metavar='FILE')
+    parser.add_argument('-out_path',
+                        help='Output directory',
+                        default='.',
+                        metavar='DIR')
+    parser.add_argument('-n_cpu',
+                        help='Number of CPUs to use in the prediction',
+                        default=-1,
+                        type=int,
+                        metavar='NCPU')
+    parser.add_argument('--gpu',
+                        help='Use GPU. This option overrides -n_cpu option',
+                        action='store_true')
+    parser.add_argument('--plot',
+                        help='Plot predictions. Images will be stored in the path defined by the -out_path',
+                        action='store_true')
+    parser.add_argument('--dpi',
+                        help='DPI of the produced images',
+                        default=300,
+                        type=int,
+                        metavar='DPI')
+    args = parser.parse_args()
+
+    # Check if input file exists
+    if not os.path.isfile(args.i):
+        print('ERROR: Input file does not exist!')
+        exit()
+    # Check if input is valid fasta file
+    if not is_fasta(args.i):
+        print("ERROR: Malformed fasta file. Please check input!")
+        exit()
+    # Check if output dir exists
+    if not os.path.isdir(args.out_path):
+        print("ERROR: Output directory does not exist!")
+        exit()
+
+    # Verify fasta file
+    raw_data = list(SeqIO.parse(args.i, "fasta"))
+    data = {''.join(e for e in str(entry.id) if (e.isalnum() or e == '_')): str(entry.seq) for entry in raw_data}
+    if not len(data) == len(raw_data):
+        print("ERROR: Sequence identifiers in the fasta file are not unique!")
+        exit()
+
+    print("Loading DeepCoil model...")
+    dc = DeepCoil(use_gpu=args.gpu, n_cpu=args.n_cpu)
+
+    print('Predicting...')
+    preds = dc.predict(data)
+
+    print('Writing output...')
+
+    inp_keys = set(data.keys())
+    out_keys = set(preds.keys())
+
+    if len(out_keys) < len(inp_keys):
+        print('WARNING: Predictions for some sequences were not calculated due to length limitations and/or other errors.' \
+    ' Inspect the warnings and results carefully!')
+
+    for entry in out_keys:
+        f = open(f'{args.out_path}/{entry}.out', 'w')
+        cc_pred_raw = preds[entry]['cc']
+        cc_pred = sharpen_preds(cc_pred_raw)
+        hept_pred = preds[entry]['hept']
+        f.write('aa\tcc\traw_cc\tprob_a\tprob_d\n')
+        for aa, cc_prob, cc_prob_raw, a_prob, d_prob in zip(data[entry], cc_pred, cc_pred_raw, hept_pred[:, 1], hept_pred[:, 2]):
+            f.write('{0}\t{1:.3f}\t{2:.3f}\t{3:.3f}\t{4:.3f}\n'.format(aa, float(cc_prob), float(cc_prob_raw), float(a_prob), float(d_prob)))
+        f.close()
+    if args.plot:
+        for entry in out_keys:
+            plot_preds(preds[entry], out_file=f'{args.out_path}/{entry}.png', dpi=args.dpi)
+    print("Done!")
+