diff --git a/notebooks/resource-allocation/upload_df.py b/notebooks/resource-allocation/upload_df.py new file mode 100644 index 000000000..82cfe9a01 --- /dev/null +++ b/notebooks/resource-allocation/upload_df.py @@ -0,0 +1,74 @@ +import pandas as pd + +# Example data loading +filename = './data/jobs_2024-02-21.tsv.gz' +df = pd.read_csv(filename, sep='\t', dtype={'extra_info': str}) + +# Convert string to timedelta, then to total seconds +df['ElapsedRawTime'] = pd.to_timedelta( + df['ElapsedRawTime']).apply( + lambda x: x.total_seconds()) + +cname = "Validate" +sname = "Diversity types - alpha_vector" +df = df[(df.cName == cname) & (df.sName == sname)] + +columns_to_check = ['samples', 'columns', 'input_size', 'MaxRSSRaw', 'ElapsedRawTime'] +df = df.dropna(subset=columns_to_check) + + +COL_NAME = 'samples * columns' +df[COL_NAME] = df['samples'] * df['columns'] +columns = ["MaxRSSRaw", "ElapsedRawTime"] +max_rows = [] + +for curr in columns: + # Get the maximum value for 'curr' within each COL_NAME group + max_values = df.groupby(COL_NAME)[curr].transform(max) + # Filter rows where the current column's value + # is the maximum within its group + curr_rows = df[df[curr] == max_values] + max_rows.append(curr_rows) + +filtered_df = pd.concat(max_rows).drop_duplicates().reset_index(drop=True) + + +print(len(filtered_df)) + + +# INSERT INTO qiita.processing_job(processing_job_id, email, command_id, +# command_parameters, processing_job_status_id) +# VALUES('ca27ddbc-a678-4b09-8a1d-b65f52f8eb49', +# 'admin@foo.com', 1, '""'::json, 1); + +# INSERT INTO qiita.slurm_resource_allocations(processing_job_id, samples, +# columns, input_size, extra_info, memory_used, walltime_used) +# VALUES('ca27ddbc-a678-4b09-8a1d-b65f52f8eb49', 39, 81, 2, 'nan', +# 327036000, 91); + +# processing_job_id uuid NOT NULL, +# samples integer, +# columns integer, +# input_size bigint, +# extra_info varchar DEFAULT NULL, +# memory_used bigint, +# walltime_used integer, + +res = "" +print(df.ElapsedRaw, df.ElapsedRawTime) +for index, row in filtered_df.iterrows(): + res += f"""('{row['QiitaID']}', 'admin@foo.bar', 1, '""'::json, 1),\n""" +res += ";\n" +res += "Split\n" +for index, row in filtered_df.iterrows(): + res += ( + f"('{row['QiitaID']}', {int(row['samples'])}, " + f"{int(row['columns'])}, {int(row['input_size'])}, " + f"'{row['extra_info']}', {int(row['MaxRSSRaw'])}, " + f"{int(row['ElapsedRawTime'])}),\n" + ) + +res += ";\n" + +with open("sql.txt", 'w') as filename: + filename.write(res) diff --git a/qiita_db/support_files/populate_test_db.sql b/qiita_db/support_files/populate_test_db.sql index f8b5466b2..951dc2f52 100644 --- a/qiita_db/support_files/populate_test_db.sql +++ b/qiita_db/support_files/populate_test_db.sql @@ -1,3 +1,5 @@ + + -- Populate.sql sets the increment to begin at 10000, but all tests expect it to start at 1, so set it back to 1 for the test DB population SELECT setval('qiita.study_study_id_seq', 1, false); @@ -582,3 +584,952 @@ INSERT INTO qiita.processing_job_workflow_root (processing_job_workflow_id, proc INSERT INTO qiita.parent_processing_job (parent_id, child_id) VALUES ('b72369f9-a886-4193-8d3d-f7b504168e75', 'd19f76ee-274e-4c1b-b3a2-a12d73507c55'); + +-- SPLIT + + +-- c. Linking some of the data_types with the default_workflows; note that this +-- is fine for the test database but we are going to need to clean up and +-- insert the most up to date recommendations directly in qiita.ucsd.edu +INSERT INTO qiita.default_workflow_data_type (default_workflow_id, data_type_id) VALUES + -- data types: + -- 1 | 16S + -- 2 | 18S + -- 3 | ITS + (1, 1), + (1, 2), + (2, 2), + (3, 3); + +-- d. adding descriptions +UPDATE qiita.default_workflow + SET description = 'This accepts html Qiita!

BYE!' + WHERE default_workflow_id = 1; +UPDATE qiita.default_workflow + SET description = 'This is another description' + WHERE default_workflow_id = 2; + +INSERT INTO qiita.processing_job(processing_job_id, email, command_id, command_parameters, processing_job_status_id) + VALUES ('ca27ddbc-a678-4b09-8a1d-b65f52f8eb49', 'admin@foo.bar', 1, '""'::json, 1), +('b0f36550-d97c-4dd5-ba1b-910470062801', 'admin@foo.bar', 1, '""'::json, 1), +('b13eefd1-12b7-4c3e-a380-94ec0a3f9f91', 'admin@foo.bar', 1, '""'::json, 1), +('469c27b7-81c4-4f41-b09d-149260961227', 'admin@foo.bar', 1, '""'::json, 1), +('6f198e33-b4ae-47e9-973b-bd5e0b06c353', 'admin@foo.bar', 1, '""'::json, 1), +('045eb36c-5144-4d54-a621-23eb5dc4d6ef', 'admin@foo.bar', 1, '""'::json, 1), +('041f3278-2752-44f9-8caa-ac6ae80c98e6', 'admin@foo.bar', 1, '""'::json, 1), +('6325e683-b215-4c56-bf5a-cbbc92330067', 'admin@foo.bar', 1, '""'::json, 1), +('03dcca05-0d4f-48c5-bc7a-7ef884be8efc', 'admin@foo.bar', 1, '""'::json, 1), 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'admin@foo.bar', 1, '""'::json, 1), +('1ef4d550-afaa-44b5-a06c-65286b9f3e00', 'admin@foo.bar', 1, '""'::json, 1), +('8533d2a8-1fcf-4842-8826-c5458c002332', 'admin@foo.bar', 1, '""'::json, 1), +('ab9095d1-af6d-49d5-9c87-bc2576f06135', 'admin@foo.bar', 1, '""'::json, 1), +('61da73ff-b4ff-49a1-b775-c6215cfbd291', 'admin@foo.bar', 1, '""'::json, 1), +('6c84dcf1-c5ea-4e69-b17f-d2d5b8d48bdf', 'admin@foo.bar', 1, '""'::json, 1), +('dcb12603-4142-44d1-9a52-3ca3511e380e', 'admin@foo.bar', 1, '""'::json, 1), +('a0dd0a4d-b73f-4e9d-87dd-d29efba25336', 'admin@foo.bar', 1, '""'::json, 1); +-- SPLIT + +INSERT INTO qiita.slurm_resource_allocations(processing_job_id, samples, columns, input_size, extra_info, memory_used, walltime_used) + VALUES ('ca27ddbc-a678-4b09-8a1d-b65f52f8eb49', 39, 81, 2, 'nan', 327036000, 91), +('b0f36550-d97c-4dd5-ba1b-910470062801', 160, 212, 2, 'nan', 342204000, 219), +('b13eefd1-12b7-4c3e-a380-94ec0a3f9f91', 62, 83, 2, 'nan', 328716000, 165), 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105), +('d4d03b6f-12f1-4f47-9ad4-b5597a3e5a16', 152, 66, 2, 'nan', 373528000, 129), +('27d95aba-9b36-40c8-8d17-7ade170172aa', 36, 83, 2, 'nan', 367776000, 155), +('a8f00090-6981-4fc3-bc37-208f01e6dceb', 44, 83, 2, 'nan', 370620000, 93), +('d7562d61-7886-45b5-9fed-90fef6636e6f', 152, 83, 2, 'nan', 372888000, 100), +('e4071da6-550d-469b-91dc-f6cda658df80', 32, 64, 2, 'nan', 369900000, 155), +('c126e507-5f13-485f-9166-775ee7228f12', 28, 26, 2, 'nan', 368464000, 97), +('d73906a4-f2c7-4d03-bd55-d637365ec3bc', 138, 89, 2, 'nan', 375516000, 288), +('167efba9-a88e-493c-a2c6-0647556ba6dd', 35, 42, 2, 'nan', 369480000, 81), +('2662c176-d042-4254-bc7e-20c092fc3af1', 152, 72, 2, 'nan', 373376000, 135), +('54b19d0d-fa47-4c37-a4ea-e2be33d54c5b', 152, 80, 2, 'nan', 369868000, 92), +('8b3e969e-78c4-4a09-9767-4aa72d99ee8c', 90, 75, 2, 'nan', 368312000, 99), +('bef26557-60db-4987-b0bf-e9e0be77da37', 32, 83, 2, 'nan', 372748000, 100), +('adcf6cb4-780b-4a9d-bb7c-cd2bf12ac201', 28, 51, 2, 'nan', 370832000, 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+('a0dd0a4d-b73f-4e9d-87dd-d29efba25336', 41, 50, 2, 'nan', 301108000, 144); diff --git a/qiita_db/test/test_util.py b/qiita_db/test/test_util.py index a72918529..250380d43 100644 --- a/qiita_db/test/test_util.py +++ b/qiita_db/test/test_util.py @@ -1309,19 +1309,65 @@ def test_quick_mounts_purge(self): class ResourceAllocationPlotTests(TestCase): def setUp(self): - self.PATH_TO_DATA = ('./qiita_db/test/test_data/' 'jobs_2024-02-21.tsv.gz') - self.CNAME = "Validate" - self.SNAME = "Diversity types - alpha_vector" + self.CNAME = "Split libraries FASTQ" + self.SNAME = "QIIMEq2" self.col_name = 'samples * columns' - self.df = pd.read_csv(self.PATH_TO_DATA, sep='\t', - dtype={'extra_info': str}) + self.df = self.get_df() + + # sra.processing_job_id AS processing_job_id, + # sra.samples AS samples, + # sra.columns AS columns, + # sra.input_size AS input_size, + # sra.extra_info AS extra_info, + # sra.memory_used AS memory_used, + # sra.walltime_used AS walltime_used, + + def get_df(self): + with qdb.sql_connection.TRN: + sql = f""" + SELECT + s.name AS sName, + s.version AS sVersion, + sc.command_id AS cID, + sc.name AS cName, + pr.processing_job_id AS processing_job_id, + pr.command_parameters AS parameters, + sra.samples AS samples, + sra.columns AS columns, + sra.input_size AS input_size, + sra.extra_info AS extra_info, + sra.memory_used AS memory_used, + sra.walltime_used AS walltime_used + FROM + qiita.processing_job pr + JOIN + qiita.software_command sc ON pr.command_id = sc.command_id + JOIN + qiita.software s ON sc.software_id = s.software_id + JOIN + qiita.slurm_resource_allocations sra + ON pr.processing_job_id = sra.processing_job_id + WHERE + sc.name = '{self.CNAME}' + AND s.name = '{self.SNAME}'; + """ + + qdb.sql_connection.TRN.add(sql) + res = qdb.sql_connection.TRN.execute_fetchindex() + columns = [ + "sName", "sVersion", "cID", "cName", + "processing_job_id", "parameters", "samples", "columns", + "input_size", "extra_info", "MaxRSSRaw", "ElapsedRaw"] + df = pd.DataFrame(res, columns=columns) + return df def test_plot_return(self): + # check the plot returns correct objects fig1, axs1 = qdb.util.resource_allocation_plot( - self.PATH_TO_DATA, self.CNAME, self.SNAME, self.col_name) + self.df, self.CNAME, self.SNAME, self.col_name) self.assertIsInstance( fig1, Figure, "Returned object fig1 is not a Matplotlib Figure") diff --git a/qiita_db/util.py b/qiita_db/util.py index 07b4bdf3e..351803ea3 100644 --- a/qiita_db/util.py +++ b/qiita_db/util.py @@ -73,7 +73,7 @@ from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText -import pandas as pd + from datetime import timedelta import matplotlib.pyplot as plt import numpy as np @@ -2337,7 +2337,7 @@ def send_email(to, subject, body): smtp.close() -def resource_allocation_plot(file, cname, sname, col_name): +def resource_allocation_plot(df, cname, sname, col_name): """Builds resource allocation plot for given filename and jobs Parameters @@ -2357,9 +2357,7 @@ def resource_allocation_plot(file, cname, sname, col_name): Returns a matplotlib object with a plot """ - df = pd.read_csv(file, sep='\t', dtype={'extra_info': str}) - df['ElapsedRawTime'] = pd.to_timedelta(df.ElapsedRawTime) - df = df[(df.cName == cname) & (df.sName == sname)] + # df = df[(df.cName == cname) & (df.sName == sname)] df.dropna(subset=['samples', 'columns'], inplace=True) df[col_name] = df.samples * df['columns'] df[col_name] = df[col_name].astype(int)