check point

README.md

@@ -1,2 +1,70 @@
 # autpop
+
+## Introduction
+
 Tool for exploring models of populations with autism.
+
+The tools accompanies our "Wroten et al. Sharing parental 
+genomes by siblings concordant or discordant for autism" 
+manuscript that will hopefully be published soon. 
+
+The models and the methods used to make predictions about 
+populations of families with autism are described in detail in the 
+manuscript. 
+
+## Installation
+
+autpop is distributed as a conda package in the iossifovlab conda channel. To install one needs first to intall a conda infrastructure. 
+There are many alternative ways to accomplish that. The easiest one may be to install Anaconda from 
+https://www.anaconda.com/products/distribution.
+
+Anaconda installs a large number of packages related to Datascience. 
+If these are not needed, one can install the much smaller (and quicker to install) Miniconda from https://docs.conda.io/en/latest/miniconda.
+
+
+Once Anaconda or Miniconda are installed, one can install autpop using the following command prompt command:
+
+```shell
+$ conda install -c iossifovlab autpop
+```
+
+## Basic use
+
+autpop is a command line tool. To use it one has to define one or more population models in a yaml file. A example of a model definition is:
+
+```yaml
+threshold_model:
+  name: basic
+  male_threshold: 9
+  female_threshold: 11
+  locus_classes:
+  - w: 1
+    f: 0.4
+    n: 5
+  - w: 8
+    f: 0.01
+    n: 2
+```
+
+If we store the above in a file called simple.yaml we can compute the predicted risks and parental genomic sharing by running the following command: 
+
+```shell
+$ autpop simple.yaml
+```
+
+The intermediate and final results will be stored in three files: family_stats_simple.txt, global_stats_simple.txt and models_results.txt. These files are described in detail in the manuscript.
+
+autpop accepts many optional parameter that controll how the predictions will be computed. For a list and a description of all 
+optional parameters one can use:
+
+```shell
+$ autpop --help
+```
+
+
+
+
+## Soucre code
+
+https://github.com/iossifovlab/autpop
+

autpop/population_threshold_model.py

@@ -616,7 +616,7 @@ def compute_global_stats(all_stats, family_type_set: FamilyTypeSet,
     n_unaffected_parents_families_with_sample_based_predictions = \
         len([1 for st in all_stats
              if not st['precise_prediction'] and
-             not st['parents_affected']])
+             st['unaffected_parents']])
     global_stats['prediction_details'] = {
 
         'precise':
@@ -833,6 +833,13 @@ def cli(cli_args=None):
     parser.add_argument("-op", "--output_precision", type=int, default=3,
                         help="The precision of all float values in the "
                         "models_results.txt output table.")
+    parser.add_argument("-od", "--output_dir", type=str, default=None,
+                        help="The output directory. By default, a directory, "
+                        "next to the model definition file file will be used: "
+                        "if the model definition is "
+                        "<dir>/<model definition>.yaml, the output default "
+                        "output directory will be named "
+                        "<dir>/<model definition>_results.")
 
     args = parser.parse_args(cli_args)
 
@@ -844,8 +851,10 @@ def cli(cli_args=None):
         modelsD = {m.model_name: m for m in models}
         models = [modelsD[mn] for mn in args.model]
 
-    pre, _ = os.path.splitext(args.models_file)
-    out_dir = pathlib.Path(pre + "_results")
+    out_dir = args.out_dir
+    if not out_dir:
+        pre, _ = os.path.splitext(args.models_file)
+        out_dir = pathlib.Path(pre + "_results")
     out_dir.mkdir(parents=True, exist_ok=True)
 
     GSB = []

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="autpop",
-    version="0.7",
+    version="0.8",
     author="Ivan Iossifov",
     author_email="[email protected]",
     description="Autism population model",