From 4865289f4a12fedf89d016b6084e674de7481936 Mon Sep 17 00:00:00 2001
From: alexholehouse <alex.holehouse@gmail.com>
Date: Tue, 11 May 2021 01:15:43 -0500
Subject: [PATCH 1/3] Removed hidden .DS_Store file and updated gitignore so
 they'll be ignored in the future

---
 .gitignore        |   1 +
 scripts/.DS_Store | Bin 6148 -> 0 bytes
 2 files changed, 1 insertion(+)
 delete mode 100644 scripts/.DS_Store

diff --git a/.gitignore b/.gitignore
index 7428e9a..b2b3334 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,7 @@ __pycache__/
 
 # C extensions
 *.so
+.DS_Store
 
 # Distribution / packaging
 .Python
diff --git a/scripts/.DS_Store b/scripts/.DS_Store
deleted file mode 100644
index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 6148
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From b6d9bad63408f1627a7bdbd25e1d035d92cdb659 Mon Sep 17 00:00:00 2001
From: alexholehouse <alex.holehouse@gmail.com>
Date: Tue, 11 May 2021 01:20:28 -0500
Subject: [PATCH 2/3] Removed some unecessary imports in uniprot_predictions
 and updated documentation slightly there re:function sig.

---
 metapredict/backend/uniprot_predictions.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/metapredict/backend/uniprot_predictions.py b/metapredict/backend/uniprot_predictions.py
index 50f0af8..949c79a 100644
--- a/metapredict/backend/uniprot_predictions.py
+++ b/metapredict/backend/uniprot_predictions.py
@@ -1,18 +1,14 @@
 # code for pulling down uniprot sequence for predictions
-import os
-import sys
-import argparse
 import urllib3
 
-import csv
-import protfasta
-
-from metapredict import meta
-
 def fetch_sequence(uniprot_id):
     """
     Function that returns the amino acid sequence by polling UniProt.com
     
+    Note that right now the test for success is a bit hap-hazard (looks for the
+    string "Sorry", which appears if the UniProt call fails. We probably want
+    something a bit more robust in the future...
+    
     Parameters
     --------------
     uniprot_id : str
@@ -20,6 +16,9 @@ def fetch_sequence(uniprot_id):
 
     Returns
     -----------
+    str or None:
+        If the call is succesfull, this returns the amino acid string. If not, it returns
+        None. 
     
     """
 

From ded69137cf36919544dec207b97124a3bbdb3a91 Mon Sep 17 00:00:00 2001
From: alexholehouse <alex.holehouse@gmail.com>
Date: Tue, 11 May 2021 01:29:04 -0500
Subject: [PATCH 3/3] added sneaky performance testing function

---
 metapredict/__init__.py | 57 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 57 insertions(+)

diff --git a/metapredict/__init__.py b/metapredict/__init__.py
index a3fe759..cd196df 100644
--- a/metapredict/__init__.py
+++ b/metapredict/__init__.py
@@ -16,9 +16,66 @@
 
 
 
+
+
 # Handle versioneer
 from ._version import get_versions
 versions = get_versions()
 __version__ = versions['version']
 __git_revision__ = versions['full-revisionid']
 del get_versions, versions
+
+
+def print_performance(seq_len=500, num_seqs=100, verbose=True):
+    """
+    Function that lets you test metapredicts performance on your local hardware.
+
+    Parameters
+    --------------
+    seqlen : int 
+        Length of each random sequence to be tested. Default = 500.
+
+    num_seqs : int
+        Number of sequences to compute over. Default = 100.
+
+    verbose : bool
+        Flag which, if true, means the function prints a summary when finished. If 
+        false simply returns an integer
+
+    Returns
+    ---------------
+    int
+        Returns the nearest number of sequences-per-second metapredict is currently
+        predicting. For ref, on a spring 2020 MBP this value was ~10,000 sequences per
+        second.
+
+    """
+
+    # this is a bit bad but, only import random is this FX is called
+    import random
+    import time
+    VALID_AMINO_ACIDS = ['A','C','D','E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y']
+
+    def genseq(n):
+        """
+        Function that generates a random 
+        """    
+        return "".join([random.choice(VALID_AMINO_ACIDS) for i in range(n)])
+
+    seqs = []
+    for i in range(num_seqs):
+        seqs.append(genseq(seq_len))
+
+    start = time.time()
+    for i in seqs:
+        predict_disorder(i)
+
+    end = time.time()
+    s_per_second = (seq_len*num_seqs)/(end - start)
+
+    if verbose:
+        print('Predicting %i sequences per second!'%(s_per_second))
+
+    return s_per_second
+    
+