Merge branch 'main' into 8-new-metric-request-of-datasets-that-exist-on-bildata

Author: icaoberg

AUTHORS

@@ -4,7 +4,17 @@ Ivan Cao-Berg (icaoberg) - Pittsburgh Supercomputing Center
 ## Undergraduate Students
 Eduardo J. Figueroa (EduardoJFigueroa) - University of Puerto Rico 
 
-# SAMS Data Science Group
+## CS Scholars Data Science Group
+Joshua Franco (francojoshua) - Western High School
+Nicolas Watkins (nicolasw-cmu) - Edmond Santa Fe Highschool
+Carmen Ung (cmucung) - Rosemead High School 
+Marcuslyne Sieh (marcuslynes) - The Metropolitan Regional Career and Technical Center
+Manav Mahida (ManavMahida) - North Penn High School
+Temidayo Ogundare (Togundar) - Science Park High School
+Neptune Barton (jellyfishking-github)- Eldorado High School
+
+
+## SAMS Data Science Group
 Tiffany Wang (Tffny3) - Cupertino High School 
 Eileen Lin (eileen-png) - Eleanor Roosevelt High School, eSTEM Academy
 Louis Lin (Bobvius) - Jack Britt High

braininventory/__init__.py

@@ -1 +1 @@
-from .get import *
+from .get import *

braininventory/get.py

@@ -3,6 +3,7 @@
 import json
 from datetime import date
 
+
 def today():
     """
 	Get today's snapshot of Brain Image Library.
@@ -23,95 +24,130 @@ def today():
     else:
         print("Error: Failed to fetch JSON data")
         return pd.DataFrame()
-    
+
+
 def __get_number_of_datasets(df):
-     return len(df)
+    return len(df)
+
 
 def __get_completeness_score(df):
-      return df['score'].sum()/len(df)
+    return df["score"].sum() / len(df)
+
 
 def __is_reachable(url):
-	response = requests.get(url)
+    response = requests.get(url)
+
+    if response.status_code == 200:
+        return True
+    else:
+        return False
 
-	if response.status_code == 200:
-		return True
-	else:
-		return False
 
 def __get_metadata_version(df):
-    return df['metadata_version'] .value_counts().to_dict()
+    return df["metadata_version"].value_counts().to_dict()
+
 
 def __get_contributor(df):
-    return df['contributor'].value_counts().to_dict()
+    return df["contributor"].value_counts().to_dict()
+
 
 def __get_affilation(df):
-    return df['affiliation'].value_counts().to_dict()
+    return df["affiliation"].value_counts().to_dict()
+
+def __get_awards(df):
+    return df["award_number"].unique()
 
 def __get_award_number(df):
-    return df['award_number'].value_counts().to_dict()
+    return df["award_number"].value_counts().to_dict()
+
 
 def __get_species(df):
-    return df['species'].value_counts().to_dict()
+    return df["species"].value_counts().to_dict()
+
 
 def __get_cnbtaxonomy(df):
-    return df['cnbtaxonomy'].value_counts().to_dict()
+    return df["cnbtaxonomy"].value_counts().to_dict()
+
 
 def __get_samplelocalid(df):
-    return df['samplelocalid'].value_counts().to_dict()
+    return df["samplelocalid"].value_counts().to_dict()
+
 
 def __get_genotype(df):
-    return df['genotype'].value_counts().to_dict()
+    return df["genotype"].value_counts().to_dict()
+
 
 def __get_generalmodality(df):
-    return df['generalmodality'].value_counts().to_dict()
+    return df["generalmodality"].value_counts().to_dict()
+
 
 def __get_technique(df):
-    return df['technique'].value_counts().to_dict()
+    return df["technique"].value_counts().to_dict()
+
 
 def __get_locations(df):
-    return df['locations'].value_counts().to_dict()
+    return df["locations"].value_counts().to_dict()
+
+def __get_contributors(df):
+    """
+    This returns an array of contributor names from the contributorname column.
+    """
+    return df["contributorname"].unique()
+
+
+def __get_project_names(df):
+	'''
+	Gets the unique list of project names.
+
+    Input: dataframe
+    Output: list 
+    '''
+	return df['project'].unique()
+
+def __get_list_of_projects(df):
+    '''
+    Get the list of names for unique projects
+
+    Input parameter: dataframe
+    Output:  list of projects
+    '''
+    
+    return df['project'].unique().to_dict()
+
+def __get_number_of_projects(df):
+    '''
+    Get the number of unique projects
+
+    Input parameter: dataframe
+    Output:  number of projects
+    '''
+    
+    return len(df['project'].unique())
 
 def report():
     # Get today's date
-	tdate = date.today()
-
-	# Convert date to string
-	tdate = tdate.strftime("%Y-%m-%d")
-        
-	df = today()
-
-	report = {}
-	report['date'] = tdate
-	report['number_of_datasets'] = __get_number_of_datasets(df)
-	report['completeness_score'] = __get_completeness_score(df)
-	report['metadata_version'] = __get_metadata_version(df)
-    report['contributor'] =  __get_contributor(df)
-    report['affiliation'] =  __get_affilation(df)
-    report['award_number'] = __get_award_number(df)
-    report['species'] = __get_species(df)
-    report['cnbtaxonomy'] = __get_cnbtaxonomy(df)
-    report['samplelocalid'] = __get_samplelocalid(df)
-    report['genotype'] = __get_genotype(df)
-    report['generalmodality'] = __get_generalmodality(df)
-    report['technique'] = __get_technique(df)
-    report['locations'] = __get_locations(df)
-     
-	report['is_reachable'] = df['URL'].apply(__is_reachable)
-
-	return report
-
-#The following block is a function that finds the number of rows that have 'true' under the key 'exists'.
-def __get_exists_true(df):
-  return len(df[df['exists']== True]) #The true listed in the dataframe is the Boolean true value.
-print(__get_exists_true(df))
-
-#The following block is a function that finds the number of total rows.
-def __get_exists_total(df):
-  return len(df) #len counts the number of rows in the dataframe.
-print(__get_exists_total(df))
-
-#Now that we have the total number of exists and the total number of rows in the dataframe we can find the fraction of the total that exist using simple division.
-proportion = exists_true/exists_total 
-print(proportion)
-print(f'The proportion of samples that exists is equal to ' + str(proportion) + '.') #The proportion is a variable with a numerical value that must be casted to add to other string objects in the print function.
-
+    tdate = date.today()
+
+    # Convert date to string
+    tdate = tdate.strftime("%Y-%m-%d")
+
+    df = today()
+
+    report = {}
+    report["date"] = tdate
+    report["number_of_datasets"] = __get_number_of_datasets(df)
+    report["completeness_score"] = __get_completeness_score(df)
+    report["metadata_version"] = __get_metadata_version(df)
+    report["contributor"] = __get_contributor(df)
+    report["affiliation"] = __get_affilation(df)
+    report["award_number"] = __get_award_number(df)
+    report["species"] = __get_species(df)
+    report["cnbtaxonomy"] = __get_cnbtaxonomy(df)
+    report["samplelocalid"] = __get_samplelocalid(df)
+    report["genotype"] = __get_genotype(df)
+    report["generalmodality"] = __get_generalmodality(df)
+    report["technique"] = __get_technique(df)
+    report["locations"] = __get_locations(df)
+    report["is_reachable"] = df["URL"].apply(__is_reachable)
+
+    return report