[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
isudatateam · Sep 2, 2024 · e12c0ba · e12c0ba
1 parent 03ca984
commit e12c0ba
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diff --git a/scripts/auth/drive2webaccess.py b/scripts/auth/drive2webaccess.py
@@ -2,9 +2,10 @@
 Synchronize the ACL on the Google Drive to the local DB
 """
 
-import isudatateam.cscap_utils as utils
 from pyiem.util import get_dbconn
 
+import isudatateam.cscap_utils as utils
+
 
 def main():
     """Go Main Go."""

diff --git a/scripts/cscap/add_agronomic_column.py b/scripts/cscap/add_agronomic_column.py
@@ -1,4 +1,5 @@
 import gdata.docs.client
+
 import isudatateam.cscap_utils as util
 
 config = util.get_config()

diff --git a/scripts/cscap/check_2012_on.py b/scripts/cscap/check_2012_on.py
@@ -3,10 +3,11 @@
 2011 and the rest of the years
 """
 
-import isudatateam.cscap_utils as util
 from gspread_pandas import Spread
 from pyiem.util import logger
 
+import isudatateam.cscap_utils as util
+
 LOG = logger()
 
 

diff --git a/scripts/cscap/check_agronomic_header.py b/scripts/cscap/check_agronomic_header.py
@@ -1,8 +1,9 @@
 """Gio discovered a mismatch between AG codes and headers"""
 
-import isudatateam.cscap_utils as util
 import pandas as pd
 
+import isudatateam.cscap_utils as util
+
 config = util.get_config()
 drive = util.get_driveclient(config, "cscap")
 sheets = util.get_sheetsclient(config, "cscap")

diff --git a/scripts/cscap/chicago delta yield biomass.ipynb b/scripts/cscap/chicago delta yield biomass.ipynb
@@ -14,9 +14,12 @@
     }
    ],
    "source": [
-    "import psycopg2\n",
     "import pandas as pd\n",
-    "pgconn = psycopg2.connect(database='sustainablecorn', host='iemdb', user='nobody')\n",
+    "import psycopg2\n",
+    "\n",
+    "pgconn = psycopg2.connect(\n",
+    "    database=\"sustainablecorn\", host=\"iemdb\", user=\"nobody\"\n",
+    ")\n",
     "cursor = pgconn.cursor()\n",
     "pd.set_printoptions(max_rows=400, max_columns=10)\n",
     "cursor.execute(\"\"\"\n",
@@ -27,26 +30,31 @@
     "\"\"\")\n",
     "data = {}\n",
     "for row in cursor:\n",
-    "    key = '%s|%s|%s' % (row[0], row[1], row[3])\n",
+    "    key = \"%s|%s|%s\" % (row[0], row[1], row[3])\n",
     "    if not data.has_key(key):\n",
-    "        data[key] = {'rotation': row[5], 'tillage': row[6]}\n",
-    "    data[key][ row[2] ] = float(row[4])\n",
+    "        data[key] = {\"rotation\": row[5], \"tillage\": row[6]}\n",
+    "    data[key][row[2]] = float(row[4])\n",
     "\n",
     "rows = []\n",
     "for key in data.keys():\n",
     "    tokens = key.split(\"|\")\n",
-    "    rows.append( dict(siteid=tokens[0], plotid=row[1], year=tokens[2], \n",
-    "                      agr7=data[key].get('AGR7'), \n",
-    "                      agr17=data[key].get('AGR17'), \n",
-    "                      agr19=data[key].get('AGR19'), \n",
-    "                      agr39=data[key].get('AGR39'),\n",
-    "                      rotation=data[key]['rotation'],\n",
-    "                      tillage=data[key]['tillage']\n",
-    "                      ) )\n",
-    "    \n",
+    "    rows.append(\n",
+    "        dict(\n",
+    "            siteid=tokens[0],\n",
+    "            plotid=row[1],\n",
+    "            year=tokens[2],\n",
+    "            agr7=data[key].get(\"AGR7\"),\n",
+    "            agr17=data[key].get(\"AGR17\"),\n",
+    "            agr19=data[key].get(\"AGR19\"),\n",
+    "            agr39=data[key].get(\"AGR39\"),\n",
+    "            rotation=data[key][\"rotation\"],\n",
+    "            tillage=data[key][\"tillage\"],\n",
+    "        )\n",
+    "    )\n",
+    "\n",
     "df = pd.DataFrame(rows)\n",
     "df.fillna(np.nan)\n",
-    "print('Loaded %s rows from the database!' % (len(df),))"
+    "print(\"Loaded %s rows from the database!\" % (len(df),))"
    ]
   },
   {
@@ -71,27 +79,56 @@
     "        return None\n",
     "    return np.ma.average(ar[ar.notnull()])\n",
     "\n",
+    "\n",
     "rows = []\n",
     "for year in [\"2011\", \"2012\", \"2013\"]:\n",
     "    for sid in df.siteid.unique():\n",
-    "        agr7 = df[(df.siteid==sid)&(df.year==year)].agr7\n",
+    "        agr7 = df[(df.siteid == sid) & (df.year == year)].agr7\n",
     "        if len(agr7[agr7.notnull()]) == 0:\n",
     "            continue\n",
     "        agr7 = np.average(agr7[agr7.notnull()])\n",
-    "        \n",
-    "        cyield_nocc = a( df[(df.siteid==sid)&(df.year==year)&\n",
-    "                       ((df.rotation=='ROT4')|(df.rotation=='ROT5'))].agr17)\n",
-    "        syield_nocc = a( df[(df.siteid==sid)&(df.year==year)&\n",
-    "                       ((df.rotation=='ROT4')|(df.rotation=='ROT5'))].agr19)\n",
-    "        cyield_cc = a( df[(df.siteid==sid)&(df.year==year)&\n",
-    "                       ((df.rotation=='ROT36')|(df.rotation=='ROT37'))].agr17)\n",
-    "        syield_cc = a( df[(df.siteid==sid)&(df.year==year)&\n",
-    "                       ((df.rotation=='ROT36')|(df.rotation=='ROT37'))].agr19)\n",
     "\n",
-    "        rows.append( dict(siteid=sid, year=year, ryebio=agr7, \n",
-    "                          cyield_nocc=cyield_nocc, syield_nocc=syield_nocc, \n",
-    "                          cyield_cc=cyield_cc,     syield_cc=syield_cc) )\n",
-    "        \n",
+    "        cyield_nocc = a(\n",
+    "            df[\n",
+    "                (df.siteid == sid)\n",
+    "                & (df.year == year)\n",
+    "                & ((df.rotation == \"ROT4\") | (df.rotation == \"ROT5\"))\n",
+    "            ].agr17\n",
+    "        )\n",
+    "        syield_nocc = a(\n",
+    "            df[\n",
+    "                (df.siteid == sid)\n",
+    "                & (df.year == year)\n",
+    "                & ((df.rotation == \"ROT4\") | (df.rotation == \"ROT5\"))\n",
+    "            ].agr19\n",
+    "        )\n",
+    "        cyield_cc = a(\n",
+    "            df[\n",
+    "                (df.siteid == sid)\n",
+    "                & (df.year == year)\n",
+    "                & ((df.rotation == \"ROT36\") | (df.rotation == \"ROT37\"))\n",
+    "            ].agr17\n",
+    "        )\n",
+    "        syield_cc = a(\n",
+    "            df[\n",
+    "                (df.siteid == sid)\n",
+    "                & (df.year == year)\n",
+    "                & ((df.rotation == \"ROT36\") | (df.rotation == \"ROT37\"))\n",
+    "            ].agr19\n",
+    "        )\n",
+    "\n",
+    "        rows.append(\n",
+    "            dict(\n",
+    "                siteid=sid,\n",
+    "                year=year,\n",
+    "                ryebio=agr7,\n",
+    "                cyield_nocc=cyield_nocc,\n",
+    "                syield_nocc=syield_nocc,\n",
+    "                cyield_cc=cyield_cc,\n",
+    "                syield_cc=syield_cc,\n",
+    "            )\n",
+    "        )\n",
+    "\n",
     "df2 = pd.DataFrame(rows)"
    ]
   },
@@ -396,7 +433,7 @@
     }
    ],
    "source": [
-    "df2.sort('ryebio')"
+    "df2.sort(\"ryebio\")"
    ]
   },
   {
@@ -416,11 +453,15 @@
     }
    ],
    "source": [
-    "df3 = df2[(df2.siteid!='WOOSTER.COV')]\n",
-    "(fig, ax) = plt.subplots(1,1)\n",
+    "df3 = df2[(df2.siteid != \"WOOSTER.COV\")]\n",
+    "(fig, ax) = plt.subplots(1, 1)\n",
     "\n",
-    "ax.scatter(df3.ryebio, df3.cyield_cc - df3.cyield_nocc, marker='+', s=50, label='Corn')\n",
-    "ax.scatter(df3.ryebio, df3.syield_cc - df3.syield_nocc, marker='s', s=50, label='Soy')\n",
+    "ax.scatter(\n",
+    "    df3.ryebio, df3.cyield_cc - df3.cyield_nocc, marker=\"+\", s=50, label=\"Corn\"\n",
+    ")\n",
+    "ax.scatter(\n",
+    "    df3.ryebio, df3.syield_cc - df3.syield_nocc, marker=\"s\", s=50, label=\"Soy\"\n",
+    ")\n",
     "ax.legend()\n",
     "ax.set_ylabel(\"$\\Delta$ Yield (CoverCrop minus Non) [kg/ha]\")\n",
     "ax.set_xlabel(\"Rye Spring Biomass [kg/ha]\")\n",

diff --git a/scripts/cscap/chicago.ipynb b/scripts/cscap/chicago.ipynb
@@ -14,20 +14,31 @@
     }
    ],
    "source": [
-    "import psycopg2\n",
     "import pandas as pd\n",
-    "pgconn = psycopg2.connect(database='sustainablecorn', host='iemdb', user='nobody')\n",
+    "import psycopg2\n",
+    "\n",
+    "pgconn = psycopg2.connect(\n",
+    "    database=\"sustainablecorn\", host=\"iemdb\", user=\"nobody\"\n",
+    ")\n",
     "cursor = pgconn.cursor()\n",
     "pd.set_printoptions(max_rows=400, max_columns=10)\n",
     "cursor.execute(\"\"\"\n",
     " SELECT uniqueid, operation, to_char(valid, 'Mon dd,YYYY'), cropyear, valid from operations ORDER by valid ASC\n",
     "\"\"\")\n",
     "rows = []\n",
     "for row in cursor:\n",
-    "    rows.append( dict(siteid=row[0], year=row[3], operation=row[1], date=row[4], sdate=row[2]) )\n",
-    "    \n",
+    "    rows.append(\n",
+    "        dict(\n",
+    "            siteid=row[0],\n",
+    "            year=row[3],\n",
+    "            operation=row[1],\n",
+    "            date=row[4],\n",
+    "            sdate=row[2],\n",
+    "        )\n",
+    "    )\n",
+    "\n",
     "df = pd.DataFrame(rows)\n",
-    "print('Loaded %s rows from the database!' % (len(df),))"
+    "print(\"Loaded %s rows from the database!\" % (len(df),))"
    ]
   },
   {
@@ -241,12 +252,14 @@
     }
    ],
    "source": [
-    "df2 = df[(\n",
-    "          (df.operation == 'termination_rye_corn')|\n",
-    "          (df.operation == 'termination_rye_soy'))\n",
-    "         &(df.year==2013)\n",
-    "        ].sort(['date'])\n",
-    "df2[['sdate', 'operation', 'siteid', 'year']]"
+    "df2 = df[\n",
+    "    (\n",
+    "        (df.operation == \"termination_rye_corn\")\n",
+    "        | (df.operation == \"termination_rye_soy\")\n",
+    "    )\n",
+    "    & (df.year == 2013)\n",
+    "].sort([\"date\"])\n",
+    "df2[[\"sdate\", \"operation\", \"siteid\", \"year\"]]"
    ]
   },
   {
@@ -779,8 +792,8 @@
     }
    ],
    "source": [
-    "df2 = df[df.operation == 'plant_corn']\n",
-    "df2[['date', 'operation', 'siteid', 'year']]"
+    "df2 = df[df.operation == \"plant_corn\"]\n",
+    "df2[[\"date\", \"operation\", \"siteid\", \"year\"]]"
    ]
   },
   {
@@ -889,8 +902,8 @@
     }
    ],
    "source": [
-    "df2 = df[df.operation == 'sample_soilnitrate']\n",
-    "df2[['date', 'operation', 'siteid', 'year']]"
+    "df2 = df[df.operation == \"sample_soilnitrate\"]\n",
+    "df2[[\"date\", \"operation\", \"siteid\", \"year\"]]"
    ]
   },
   {

diff --git a/scripts/cscap/create_agronomic_datasheet.py b/scripts/cscap/create_agronomic_datasheet.py
@@ -5,6 +5,7 @@
 import copy
 
 import gdata.spreadsheets.data
+
 import isudatateam.cscap_utils as util
 
 config = util.get_config()

diff --git a/scripts/cscap/create_soil_bd.py b/scripts/cscap/create_soil_bd.py
@@ -1,4 +1,5 @@
 import gdata.spreadsheets.data
+
 import isudatateam.cscap_utils as util
 
 config = util.get_config()

diff --git a/scripts/cscap/create_soil_nitrate_sheets.py b/scripts/cscap/create_soil_nitrate_sheets.py
@@ -1,4 +1,5 @@
 import gdata.spreadsheets.data
+
 import isudatateam.cscap_utils as util
 
 config = util.get_config()

diff --git a/scripts/cscap/create_soil_texture_sheets.py b/scripts/cscap/create_soil_texture_sheets.py
@@ -3,6 +3,7 @@
 """
 
 import gdata.spreadsheets.data
+
 import isudatateam.cscap_utils as util
 
 config = util.get_config()

diff --git a/scripts/cscap/dwm1.ipynb b/scripts/cscap/dwm1.ipynb
@@ -9,8 +9,8 @@
    "outputs": [],
    "source": [
     "import psycopg2\n",
-    "import numpy as np\n",
     "from pandas.io.sql import read_sql\n",
+    "\n",
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt"
    ]
@@ -29,17 +29,23 @@
     }
    ],
    "source": [
-    "pgconn = psycopg2.connect(database='sustainablecorn', host='iemdb', user='nobody')\n",
-    "site = 'STJOHNS'\n",
-    "df = read_sql(\"\"\"\n",
+    "pgconn = psycopg2.connect(\n",
+    "    database=\"sustainablecorn\", host=\"iemdb\", user=\"nobody\"\n",
+    ")\n",
+    "site = \"STJOHNS\"\n",
+    "df = read_sql(\n",
+    "    \"\"\"\n",
     "    SELECT o.valid, o.plotid, o.discharge_mm as discharge_mm,\n",
     "    lag(o.discharge_mm) OVER (PARTITION by o.plotid ORDER by o.valid ASC) as discharge_mm_lag,\n",
     "    t.depth_mm as depth_mm, lag(t.depth_mm) OVER (PARTITION by o.plotid ORDER by o.valid ASC)\n",
     "    as depth_mm_lag from tileflow_data o JOIN watertable_data t\n",
     "    on (date_trunc('minute', o.valid) = date_trunc('minute', t.valid)\n",
     "        and  t.plotid = o.plotid ) WHERE o.uniqueid = %s and\n",
     "    discharge_mm is not null and depth_mm is not null ORDER by plotid ASC, valid ASC\n",
-    "\"\"\", pgconn, params=(site,))\n",
+    "\"\"\",\n",
+    "    pgconn,\n",
+    "    params=(site,),\n",
+    ")\n",
     "print(len(df.index))"
    ]
   },
@@ -81,11 +87,11 @@
    "source": [
     "(fig, ax) = plt.subplots(2, 1, figsize=(12, 24))\n",
     "\n",
-    "plots = df['plotid'].unique()\n",
+    "plots = df[\"plotid\"].unique()\n",
     "plots.sort()\n",
     "for i, plotid in enumerate(plots):\n",
-    "    df2 = df[(df['plotid']==plotid) & (df['depth_mm'] > df['depth_mm_lag'])]\n",
-    "    ax[i].scatter( df2['depth_mm'], df2['discharge_mm'])\n",
+    "    df2 = df[(df[\"plotid\"] == plotid) & (df[\"depth_mm\"] > df[\"depth_mm_lag\"])]\n",
+    "    ax[i].scatter(df2[\"depth_mm\"], df2[\"discharge_mm\"])\n",
     "    ax[i].set_ylabel(\"Discharge [mm]\")\n",
     "    ax[i].grid(True)\n",
     "    ax[i].set_ylim(0, 0.21)\n",
@@ -124,12 +130,14 @@
    "source": [
     "(fig, ax) = plt.subplots(6, 1, figsize=(12, 24))\n",
     "\n",
-    "plots = df['plotid'].unique()\n",
+    "plots = df[\"plotid\"].unique()\n",
     "plots.sort()\n",
     "for i, plotid in enumerate(plots):\n",
-    "    df2 = df[(df['plotid']==plotid)]\n",
-    "    ax[i].scatter( df2['depth_mm'] - df2['depth_mm_lag'],\n",
-    "                  df2['discharge_mm'] - df2['discharge_mm_lag'])\n",
+    "    df2 = df[(df[\"plotid\"] == plotid)]\n",
+    "    ax[i].scatter(\n",
+    "        df2[\"depth_mm\"] - df2[\"depth_mm_lag\"],\n",
+    "        df2[\"discharge_mm\"] - df2[\"discharge_mm_lag\"],\n",
+    "    )\n",
     "    ax[i].set_ylabel(\"Discharge [mm]\")\n",
     "    ax[i].grid(True)\n",
     "    ax[i].set_ylim(-0.2, 0.2)\n",