Run through in windows.

Former-commit-id: 852d2974

Flaskserver/libs/setup.py

 from distutils.core import setup, Extension
 import numpy.distutils.misc_util
 
-c_ext = Extension('_lsh', ['../lsh-fast/_lsh.cpp', '../lsh-fast/lsh.cpp'])
+c_ext = Extension('_lsh', ['../../lsh-fast/_lsh.cpp', '../../lsh-fast/lsh.cpp'])
 
 setup(
     name='lsh',

Flaskserver/preprocessing.py

 import numpy as np
 import pandas as pd
-from libs import bigwig
-import bbi
+# from libs import bigwig
+# import bbi
 import dask.dataframe as dd
 import os.path
 from sklearn import preprocessing

README.md

@@ -21,14 +21,18 @@ All dependencies are listed in the *environment.yml* file. To create an environm
 This will create a conda environment named *pseudo*. Now activate the environment as follows:
 `conda activate pseudo`
 
-### Step 2: Creating the LSH package
+### Step 2: Prepare backend - creating the LSH package
 The LSH algorithm is maintained locally for now, so you'll have to create it manually. The file that you need to setup this package is located in the Flaskserver folder (this is more efficient when debugging, as for every change you have to rebuild the package). So the package can be created by running the following code:
 `cd Flaskserver`
 `python3 setup.py build_ext --inplace && python3 setup.py install`
 `cd ..`
 **NOTE 1**: So as a reminder, don't forget to run the 2nd line everytime you change something in the c++ code.
 
-### Step 3: Running PSEUDo
+### Step 3: Prepare frontend - install Node packages
+The cloned Angular repository cannot be used directly. You have to install the node packages via
+`cd Angular`
+
+### Step 4: Running PSEUDo
 As mentioned before, PSEUDo exists of a gui and a server. A Makefile is provided to setup both easily. Just run the following code for the server and gui respectively:
 `make server`
 `make gui`

environment.yml

@@ -5,17 +5,20 @@ channels:
   - defaults
 
 dependencies:
+  - python
+  - dask[dataframe]
   - flask
-  - numpy
-  - pandas
   - flask_cors
-  - orjson
-  - dask[dataframe]
   - jupyter
+  - matplotlib
+  - numpy
+  - orjson
+  - pandas
   - pip
+  - scikit-learn
+  - tslearn
   - pip:
-      - pybbi
-      - sklearn
-      - cooler
+#      - pybbi
+#      - cooler
       - ucrdtw
       - dtw-python

lsh-fast/lsh.cpp

@@ -378,7 +378,7 @@ int main() {
     srand((unsigned int)time(NULL));
     clock_t begin_time = clock();
     FILE *data;
-    int M = 125621;
+    const int M = 125621;
     int K = 2; //ln((ln(0.5))/ln(1-exp(-2*(0.1)^2*120)))/ln((1-exp(-2*(0.1)^2*120))/(0.5))
     int L = 6; //ln(0.05)/(ln(1-(1-exp(-2(0.1)^2*120))^4))0.0653330009864134
     int T = 120;
@@ -397,7 +397,8 @@ int main() {
     data = fopen("processed-data","r");
     if( data == NULL )
         exit(2);
-    char d[M]; int i; int j = 0; float f;
+    char d[M];
+    int i; int j = 0; float f;
     while(fscanf(data,"%[^\n]\n",d) != EOF && j< M)
     {
         char *p =  strtok(d," ");