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README.md

@@ -1,10 +1,10 @@
 ---
 title: Tendaysforecast
-emoji: 👁
-colorFrom: red
-colorTo: gray
+emoji: 👀
+colorFrom: pink
+colorTo: green
 sdk: streamlit
-sdk_version: 1.41.1
+sdk_version: 1.31.1
 app_file: app.py
 pinned: false
 ---

app.py

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+import streamlit as st
+import plotly.express as px
+import openmeteo_requests
+import requests_cache
+import pandas as pd
+from retry_requests import retry
+from datetime import datetime, timedelta
+from support_functions import *
+import numpy as np
+from scipy import interpolate
+
+#Page Configuration
+st.set_page_config(initial_sidebar_state="collapsed", page_title='10 Days Forecast', menu_items={
+        'Get Help': None,
+        'Report a bug': None,
+        'About': "Designed by Manaruchi Mohapatra"
+    })
+
+
+st.header("Weather Forecast")
+
+#Check for Lat Lon info and Get ECMWF Data
+#Get Initial Configuration
+url_params = st.query_params
+url_params_keys = dict(url_params).keys()
+if('lat' not in url_params_keys or 'lon' not in url_params_keys):
+    latvals = 22.47
+    lonvals = 70.05
+    st.info("Latitude and Longitude values not defined. Defaulting to Jamnagar...")
+else:
+    latvals = float(url_params['lat'])
+    lonvals = float(url_params['lon'])
+
+wxdata = get_ecmwf_data(latvals, lonvals)
+
+#Get Dates list
+start_date = datetime(wxdata['Year'].values[0], wxdata['Month'].values[0], wxdata['Day'].values[0])
+end_date = start_date + timedelta(days=10)
+st.caption(f"Time Period: {parseday(start_date)} to {parseday(end_date)}")
+
+#Draw Tabs
+tab1, tab2 = st.tabs(['Overall', 'Daily'])
+
+#Min and Max temperature
+min_max_date_list = []
+min_max_min_list = []
+min_max_max_list = []
+for i in range(11):
+    cur_date = start_date + timedelta(days=i)
+    minidf = wxdata[(wxdata['Day'] == cur_date.day)&(wxdata['Month'] == cur_date.month)&(wxdata['Year'] == cur_date.year)]
+    min_max_date_list.append(cur_date)
+    min_max_min_list.append(minidf['temperature_2m'].min())
+    min_max_max_list.append(minidf['temperature_2m'].max())
+
+min_max_df = pd.DataFrame()
+min_max_df['Date'] = min_max_date_list
+min_max_df['Minimum Temperature'] = min_max_min_list
+min_max_df['Maximum Temperature'] = min_max_max_list
+
+mintempfig = px.line(min_max_df, x = 'Date', y = 'Minimum Temperature')
+maxtempfig = px.line(min_max_df, x = 'Date', y = 'Maximum Temperature')
+
+with tab1.expander("Minimum and Maximum Temperature"):
+    st.plotly_chart(mintempfig, use_container_width = True)
+    st.plotly_chart(maxtempfig, use_container_width = True)
+
+#temperature and inversion
+#Temperature plot
+tempfig = px.line(wxdata, x = 'Date_IST', y = 'temperature_2m', hover_data= 'temperature_925hPa',
+                    labels = {'Date_IST': 'Date and Time', 'temperature_2m': 'Dry Bulb Temp', 'temperature_925hPa': 'Temp at F/L025'})
+
+#Inversion
+invlist = []
+temp2mlist = wxdata['temperature_2m'].values
+temp025list = wxdata['temperature_925hPa'].values
+
+for t in range(len(temp2mlist)):
+    if((temp025list[t] - temp2mlist[t])>0):
+        invlist.append((temp025list[t] - temp2mlist[t]))
+    else:
+        invlist.append(0)
+
+wxdata['Inversion'] = invlist
+invfig = px.bar(wxdata, x='Date_IST', y='Inversion')
+
+
+with tab1.expander("Temperature and Inversion"):
+    st.plotly_chart(tempfig, use_container_width = True)
+    st.plotly_chart(invfig, use_container_width = True)
+
+#RH plot
+rhfig = px.line(wxdata, x = 'Date_IST', y = 'relative_humidity_2m', labels = {'newdate': 'Date and Time', 'relative_humidity_2m': 'RH(%age)'})
+
+with tab1.expander("Relative Humidity"):
+    st.plotly_chart(rhfig, use_container_width = True)
+
+#Cloud Plots
+cloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover', hover_data = ['cloud_cover_low', 'cloud_cover_mid', 'cloud_cover_high'],
+                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
+                                                        'cloud_cover_low': 'Low Clouds',
+                                                        'cloud_cover_mid': 'Medium Clouds',
+                                                        'cloud_cover_high': 'High Clouds'})
+cloudsfig.update_layout(yaxis_range=[0,100])
+lowcloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover_low', hover_data = ['cloud_cover', 'cloud_cover_mid', 'cloud_cover_high'],
+                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
+                                                        'cloud_cover_low': 'Low Clouds',
+                                                        'cloud_cover_mid': 'Medium Clouds',
+                                                        'cloud_cover_high': 'High Clouds'})
+midcloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover_mid', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_high'],
+                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
+                                                        'cloud_cover_low': 'Low Clouds',
+                                                        'cloud_cover_mid': 'Medium Clouds',
+                                                        'cloud_cover_high': 'High Clouds'})
+highcloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover_high', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_mid'],
+                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
+                                                        'cloud_cover_low': 'Low Clouds',
+                                                        'cloud_cover_mid': 'Medium Clouds',
+                                                        'cloud_cover_high': 'High Clouds'})
+lowcloudsfig.update_layout(yaxis_range=[0,100])
+midcloudsfig.update_layout(yaxis_range=[0,100])
+highcloudsfig.update_layout(yaxis_range=[0,100])
+
+with tab1.expander("Cloudiness"):
+    st.plotly_chart(cloudsfig, use_container_width = True)
+    st.plotly_chart(lowcloudsfig, use_container_width = True)
+    st.plotly_chart(midcloudsfig, use_container_width = True)
+    st.plotly_chart(highcloudsfig, use_container_width = True)
+
+#precipitaion plot
+pptfig = px.bar(wxdata, x = 'Date_IST', y = 'precipitation', labels = {'Date_IST': 'Date and Time', 'precipitation': 'Precipitation'})
+
+with tab1.expander("Precipitation"):
+    st.plotly_chart(pptfig, use_container_width = True)
+
+#pressure plot
+qnhfig = px.line(wxdata, x = 'Date_IST', y = 'surface_pressure', labels = {'Date_IST': 'Date and Time', 'surface_pressure': 'QNH(hPa)'})
+qnhfig.update_layout(yaxis_range=[995,1018])
+
+
+with tab1.expander("Surface Pressure"):
+    st.plotly_chart(qnhfig, use_container_width = True)
+
+
+#Daily Data
+ds = tab2.slider(
+    "Forecast for ", start_date, end_date,
+    value=start_date,
+    format="DD MMM YY")
+
+wxdata2 = wxdata[(wxdata['Day'] == ds.day)&(wxdata['Month'] == ds.month)&(wxdata['Year'] == ds.year)]
+
+with tab2.expander("Current Weather Register"):
+    st.table(makecwr(wxdata2))
+
+
+#temperature and inversion
+#Temperature plot
+tempfig = px.line(wxdata2, x = 'Date_IST', y = 'temperature_2m', hover_data= 'temperature_925hPa',
+                    labels = {'Date_IST': 'Date and Time', 'temperature_2m': 'Dry Bulb Temp', 'temperature_925hPa': 'Temp at F/L025'})
+
+#Inversion
+invlist = []
+temp2mlist = wxdata2['temperature_2m'].values
+temp025list = wxdata2['temperature_925hPa'].values
+
+for t in range(len(temp2mlist)):
+    if((temp025list[t] - temp2mlist[t])>0):
+        invlist.append((temp025list[t] - temp2mlist[t]))
+    else:
+        invlist.append(0)
+
+wxdata2['Inversion'] = invlist
+invfig = px.bar(wxdata2, x='Date_IST', y='Inversion')
+invfig.update_layout(yaxis_range=[0,5])
+
+with tab2.expander("Temperature and Inversion"):
+    st.plotly_chart(tempfig, use_container_width = True)
+    st.plotly_chart(invfig, use_container_width = True)
+
+#RH plot
+rhfig = px.line(wxdata2, x = 'Date_IST', y = 'relative_humidity_2m', labels = {'newdate': 'Date and Time', 'relative_humidity_2m': 'RH(%age)'})
+
+with tab2.expander("Relative Humidity"):
+    st.plotly_chart(rhfig, use_container_width = True)
+
+#Cloud Plots
+cloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover', hover_data = ['cloud_cover_low', 'cloud_cover_mid', 'cloud_cover_high'],
+                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
+                                                        'cloud_cover_low': 'Low Clouds',
+                                                        'cloud_cover_mid': 'Medium Clouds',
+                                                        'cloud_cover_high': 'High Clouds'})
+cloudsfig.update_layout(yaxis_range=[0,100])
+lowcloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover_low', hover_data = ['cloud_cover', 'cloud_cover_mid', 'cloud_cover_high'],
+                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
+                                                        'cloud_cover_low': 'Low Clouds',
+                                                        'cloud_cover_mid': 'Medium Clouds',
+                                                        'cloud_cover_high': 'High Clouds'})
+midcloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover_mid', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_high'],
+                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
+                                                        'cloud_cover_low': 'Low Clouds',
+                                                        'cloud_cover_mid': 'Medium Clouds',
+                                                        'cloud_cover_high': 'High Clouds'})
+highcloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover_high', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_mid'],
+                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
+                                                        'cloud_cover_low': 'Low Clouds',
+                                                        'cloud_cover_mid': 'Medium Clouds',
+                                                        'cloud_cover_high': 'High Clouds'})
+lowcloudsfig.update_layout(yaxis_range=[0,100])
+midcloudsfig.update_layout(yaxis_range=[0,100])
+highcloudsfig.update_layout(yaxis_range=[0,100])
+
+with tab2.expander("Cloudiness"):
+    st.plotly_chart(cloudsfig, use_container_width = True)
+    st.plotly_chart(lowcloudsfig, use_container_width = True)
+    st.plotly_chart(midcloudsfig, use_container_width = True)
+    st.plotly_chart(highcloudsfig, use_container_width = True)
+
+#pressure plot
+qnhfig = px.line(wxdata2, x = 'Date_IST', y = 'surface_pressure', labels = {'Date_IST': 'Date and Time', 'surface_pressure': 'QNH(hPa)'})
+qnhfig.update_layout(yaxis_range=[995,1018])
+
+
+with tab2.expander("Surface Pressure"):
+    st.plotly_chart(qnhfig, use_container_width = True)
+
+old_ht = [500,2500,5000,10000,18000,30000,35000,40000]
+new_ht = [1000, 2000, 3000, 5000, 7000, 9000, 15000, 18000, 25000, 30000]
+upper_air_df = pd.DataFrame()
+upper_air_df['Height(KM)'] = [0.3,0.6,0.9,1.5,2.1,3.0,4.5,6.0,7.5,9.0][::-1]
+#Upper Air Data
+sel_times = tab2.multiselect("Select Time for Upper Air Data (IST)", wxdata2['Hour'].values, [5,11,17,23])
+for sel_time in sel_times:
+    filter_df = wxdata2[wxdata2['Hour'] == sel_time]
+    wind_dir_list = []
+    wind_speed_list = []
+    temp_list = []
+    for level in ['200hPa', '250hPa', '300hPa', '500hPa', '700hPa', '850hPa', '925hPa', '1000hPa'][::-1]:
+        wind_dir_list.append(filter_df[f'winddirection_{level}'].values[0])
+        wind_speed_list.append(filter_df[f'windspeed_{level}'].values[0])
+        temp_list.append(int(filter_df[f'temperature_{level}'].values[0]))
+
+    new_wind_dir = interp_for_levels(old_ht, wind_dir_list, new_ht, is_wind_dir = True)[::-1]
+    new_wind_speed = interp_for_levels(old_ht, wind_speed_list, new_ht)[::-1]
+    new_temp = interp_for_levels(old_ht, temp_list, new_ht, round_to = 1)[::-1]
+
+    text_to_be_shown = []
+    for y in range(len(new_wind_dir)):
+        while(new_wind_dir[y]>360):
+            new_wind_dir[y] = new_wind_dir[y]-360
+        wwww = f"{new_wind_dir[y]:03d}/{new_wind_speed[y]:02d}({new_temp[y]:02d})"
+        text_to_be_shown.append(wwww)
+
+    upper_air_df[f"{sel_time:02d}:30Hr"] = text_to_be_shown
+
+tab2.dataframe(upper_air_df, use_container_width=True)
+
+st.success("Made by Manaruchi Mohapatra")

gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

requirements.txt

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+openmeteo-requests
+requests-cache
+retry-requests
+numpy
+pandas
+plotly
+scipy

support_functions.py

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+import openmeteo_requests
+import requests_cache
+import pandas as pd
+from retry_requests import retry
+from datetime import datetime, timedelta
+import numpy as np
+from scipy import interpolate
+
+def get_ecmwf_data(lat, lon):
+
+    # Setup the Open-Meteo API client with cache and retry on error
+    cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)
+    retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
+    openmeteo = openmeteo_requests.Client(session = retry_session)
+
+    # Make sure all required weather variables are listed here
+    # The order of variables in hourly or daily is important to assign them correctly below
+    url = "https://api.open-meteo.com/v1/ecmwf"
+    params = {
+        "latitude": lat,
+        "longitude": lon,
+        "hourly": ["temperature_2m", "relative_humidity_2m", "dew_point_2m", "precipitation", "weather_code", "surface_pressure", "cloud_cover", "cloud_cover_low", "cloud_cover_mid", "cloud_cover_high", "wind_speed_10m", "wind_direction_10m", "wind_gusts_10m", "surface_temperature", "temperature_1000hPa", "temperature_925hPa", "temperature_850hPa", "temperature_700hPa", "temperature_500hPa", "temperature_300hPa", "temperature_250hPa", "temperature_200hPa", "temperature_50hPa", "relative_humidity_1000hPa", "relative_humidity_925hPa", "relative_humidity_850hPa", "relative_humidity_700hPa", "relative_humidity_500hPa", "relative_humidity_300hPa", "relative_humidity_250hPa", "relative_humidity_200hPa", "relative_humidity_50hPa", "windspeed_1000hPa", "windspeed_925hPa", "windspeed_850hPa", "windspeed_700hPa", "windspeed_500hPa", "windspeed_300hPa", "windspeed_250hPa", "windspeed_200hPa", "windspeed_50hPa", "winddirection_1000hPa", "winddirection_925hPa", "winddirection_850hPa", "winddirection_700hPa", "winddirection_500hPa", "winddirection_300hPa", "winddirection_250hPa", "winddirection_200hPa", "winddirection_50hPa"],
+        "wind_speed_unit": "kn"
+    }
+    responses = openmeteo.weather_api(url, params=params)
+
+    # Process first location. Add a for-loop for multiple locations or weather models
+    response = responses[0]
+
+    # Process hourly data. The order of variables needs to be the same as requested.
+    hourly = response.Hourly()
+    hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()
+    hourly_relative_humidity_2m = hourly.Variables(1).ValuesAsNumpy()
+    hourly_dew_point_2m = hourly.Variables(2).ValuesAsNumpy()
+    hourly_precipitation = hourly.Variables(3).ValuesAsNumpy()
+    hourly_weather_code = hourly.Variables(4).ValuesAsNumpy()
+    hourly_surface_pressure = hourly.Variables(5).ValuesAsNumpy()
+    hourly_cloud_cover = hourly.Variables(6).ValuesAsNumpy()
+    hourly_cloud_cover_low = hourly.Variables(7).ValuesAsNumpy()
+    hourly_cloud_cover_mid = hourly.Variables(8).ValuesAsNumpy()
+    hourly_cloud_cover_high = hourly.Variables(9).ValuesAsNumpy()
+    hourly_wind_speed_10m = hourly.Variables(10).ValuesAsNumpy()
+    hourly_wind_direction_10m = hourly.Variables(11).ValuesAsNumpy()
+    hourly_wind_gusts_10m = hourly.Variables(12).ValuesAsNumpy()
+    hourly_surface_temperature = hourly.Variables(13).ValuesAsNumpy()
+    hourly_temperature_1000hPa = hourly.Variables(14).ValuesAsNumpy()
+    hourly_temperature_925hPa = hourly.Variables(15).ValuesAsNumpy()
+    hourly_temperature_850hPa = hourly.Variables(16).ValuesAsNumpy()
+    hourly_temperature_700hPa = hourly.Variables(17).ValuesAsNumpy()
+    hourly_temperature_500hPa = hourly.Variables(18).ValuesAsNumpy()
+    hourly_temperature_300hPa = hourly.Variables(19).ValuesAsNumpy()
+    hourly_temperature_250hPa = hourly.Variables(20).ValuesAsNumpy()
+    hourly_temperature_200hPa = hourly.Variables(21).ValuesAsNumpy()
+    #hourly_temperature_50hPa = hourly.Variables(22).ValuesAsNumpy()
+    hourly_relative_humidity_1000hPa = hourly.Variables(23).ValuesAsNumpy()
+    hourly_relative_humidity_925hPa = hourly.Variables(24).ValuesAsNumpy()
+    hourly_relative_humidity_850hPa = hourly.Variables(25).ValuesAsNumpy()
+    hourly_relative_humidity_700hPa = hourly.Variables(26).ValuesAsNumpy()
+    hourly_relative_humidity_500hPa = hourly.Variables(27).ValuesAsNumpy()
+    hourly_relative_humidity_300hPa = hourly.Variables(28).ValuesAsNumpy()
+    hourly_relative_humidity_250hPa = hourly.Variables(29).ValuesAsNumpy()
+    hourly_relative_humidity_200hPa = hourly.Variables(30).ValuesAsNumpy()
+    #hourly_relative_humidity_50hPa = hourly.Variables(31).ValuesAsNumpy()
+    hourly_windspeed_1000hPa = hourly.Variables(32).ValuesAsNumpy()
+    hourly_windspeed_925hPa = hourly.Variables(33).ValuesAsNumpy()
+    hourly_windspeed_850hPa = hourly.Variables(34).ValuesAsNumpy()
+    hourly_windspeed_700hPa = hourly.Variables(35).ValuesAsNumpy()
+    hourly_windspeed_500hPa = hourly.Variables(36).ValuesAsNumpy()
+    hourly_windspeed_300hPa = hourly.Variables(37).ValuesAsNumpy()
+    hourly_windspeed_250hPa = hourly.Variables(38).ValuesAsNumpy()
+    hourly_windspeed_200hPa = hourly.Variables(39).ValuesAsNumpy()
+    #hourly_windspeed_50hPa = hourly.Variables(40).ValuesAsNumpy()
+    hourly_winddirection_1000hPa = hourly.Variables(41).ValuesAsNumpy()
+    hourly_winddirection_925hPa = hourly.Variables(42).ValuesAsNumpy()
+    hourly_winddirection_850hPa = hourly.Variables(43).ValuesAsNumpy()
+    hourly_winddirection_700hPa = hourly.Variables(44).ValuesAsNumpy()
+    hourly_winddirection_500hPa = hourly.Variables(45).ValuesAsNumpy()
+    hourly_winddirection_300hPa = hourly.Variables(46).ValuesAsNumpy()
+    hourly_winddirection_250hPa = hourly.Variables(47).ValuesAsNumpy()
+    hourly_winddirection_200hPa = hourly.Variables(48).ValuesAsNumpy()
+    #hourly_winddirection_50hPa = hourly.Variables(49).ValuesAsNumpy()
+
+    hourly_data = {"date": pd.date_range(
+        start = pd.to_datetime(hourly.Time(), unit = "s", utc = True),
+        end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True),
+        freq = pd.Timedelta(seconds = hourly.Interval()),
+        inclusive = "left"
+    )}
+
+    d_vals, m_vals, y_vals, h_vals, date_vals = [],[],[],[], []
+    for d in hourly_data['date'].values:
+        cur_date = pd.Timestamp(d)
+        cur_date_ist = cur_date + timedelta(hours=5, minutes=30)
+        date_vals.append(cur_date_ist)
+        d_vals.append(cur_date_ist.day)
+        m_vals.append(cur_date_ist.month)
+        y_vals.append(cur_date_ist.year)
+        h_vals.append(cur_date_ist.hour)
+
+    hourly_data['Date_IST'] = date_vals
+    hourly_data['Day'] = d_vals
+    hourly_data['Month'] = m_vals
+    hourly_data['Year'] = y_vals
+    hourly_data['Hour'] = h_vals
+    hourly_data["temperature_2m"] = hourly_temperature_2m
+    hourly_data["relative_humidity_2m"] = hourly_relative_humidity_2m
+    hourly_data["dew_point_2m"] = hourly_dew_point_2m
+    hourly_data["precipitation"] = hourly_precipitation
+    hourly_data["weather_code"] = hourly_weather_code
+    hourly_data["surface_pressure"] = hourly_surface_pressure
+    hourly_data["cloud_cover"] = hourly_cloud_cover
+    hourly_data["cloud_cover_low"] = hourly_cloud_cover_low
+    hourly_data["cloud_cover_mid"] = hourly_cloud_cover_mid
+    hourly_data["cloud_cover_high"] = hourly_cloud_cover_high
+    hourly_data["wind_speed_10m"] = hourly_wind_speed_10m
+    hourly_data["wind_direction_10m"] = hourly_wind_direction_10m
+    hourly_data["wind_gusts_10m"] = hourly_wind_gusts_10m
+    hourly_data["surface_temperature"] = hourly_surface_temperature
+    hourly_data["temperature_1000hPa"] = hourly_temperature_1000hPa
+    hourly_data["temperature_925hPa"] = hourly_temperature_925hPa
+    hourly_data["temperature_850hPa"] = hourly_temperature_850hPa
+    hourly_data["temperature_700hPa"] = hourly_temperature_700hPa
+    hourly_data["temperature_500hPa"] = hourly_temperature_500hPa
+    hourly_data["temperature_300hPa"] = hourly_temperature_300hPa
+    hourly_data["temperature_250hPa"] = hourly_temperature_250hPa
+    hourly_data["temperature_200hPa"] = hourly_temperature_200hPa
+    #hourly_data["temperature_50hPa"] = hourly_temperature_50hPa
+    hourly_data["relative_humidity_1000hPa"] = hourly_relative_humidity_1000hPa
+    hourly_data["relative_humidity_925hPa"] = hourly_relative_humidity_925hPa
+    hourly_data["relative_humidity_850hPa"] = hourly_relative_humidity_850hPa
+    hourly_data["relative_humidity_700hPa"] = hourly_relative_humidity_700hPa
+    hourly_data["relative_humidity_500hPa"] = hourly_relative_humidity_500hPa
+    hourly_data["relative_humidity_300hPa"] = hourly_relative_humidity_300hPa
+    hourly_data["relative_humidity_250hPa"] = hourly_relative_humidity_250hPa
+    hourly_data["relative_humidity_200hPa"] = hourly_relative_humidity_200hPa
+    #hourly_data["relative_humidity_50hPa"] = hourly_relative_humidity_50hPa
+    hourly_data["windspeed_1000hPa"] = hourly_windspeed_1000hPa
+    hourly_data["windspeed_925hPa"] = hourly_windspeed_925hPa
+    hourly_data["windspeed_850hPa"] = hourly_windspeed_850hPa
+    hourly_data["windspeed_700hPa"] = hourly_windspeed_700hPa
+    hourly_data["windspeed_500hPa"] = hourly_windspeed_500hPa
+    hourly_data["windspeed_300hPa"] = hourly_windspeed_300hPa
+    hourly_data["windspeed_250hPa"] = hourly_windspeed_250hPa
+    hourly_data["windspeed_200hPa"] = hourly_windspeed_200hPa
+    #hourly_data["windspeed_50hPa"] = hourly_windspeed_50hPa
+    hourly_data["winddirection_1000hPa"] = hourly_winddirection_1000hPa
+    hourly_data["winddirection_925hPa"] = hourly_winddirection_925hPa
+    hourly_data["winddirection_850hPa"] = hourly_winddirection_850hPa
+    hourly_data["winddirection_700hPa"] = hourly_winddirection_700hPa
+    hourly_data["winddirection_500hPa"] = hourly_winddirection_500hPa
+    hourly_data["winddirection_300hPa"] = hourly_winddirection_300hPa
+    hourly_data["winddirection_250hPa"] = hourly_winddirection_250hPa
+    hourly_data["winddirection_200hPa"] = hourly_winddirection_200hPa
+    #hourly_data["winddirection_50hPa"] = hourly_winddirection_50hPa
+
+    hourly_dataframe = pd.DataFrame(data = hourly_data, index = None)
+    hourly_dataframe = hourly_dataframe.drop('date', axis = 1)
+    return hourly_dataframe
+
+def wxcode_to_text(w):
+    if(w==0): return "SKC"
+    elif(w==1): return "Mainly Clear"
+    elif(w==2): return "Partly Cloudy"
+    elif(w==3): return "Overcast"
+    elif(w==45): return "Fog"
+    elif(w==48): return "Depositing Rime Fog"
+    elif(w==51): return "Light Drizzle"
+    elif(w==53): return "Moderate Drizzle"
+    elif(w==55): return "Dense Drizzle"
+    elif(w==56): return "Light Freezing Drizzle"
+    elif(w==57): return "Dense Freezing Drizzle"
+    elif(w==61): return "Slight Rain"
+    elif(w==63): return "Moderate Rain"
+    elif(w==65): return "Heavy Rain"
+    elif(w==66): return "Light Freezing Rain"
+    elif(w==67): return "Heavy Freezing Rain"
+    elif(w==71): return "Slight Snowfall"
+
+def extractdateforcwr(d):
+    d = pd.Timestamp(d)
+    day, month, year, hour = d.day, d.month, d.year, d.hour
+    mtext = "Jan"
+    if(month == 1): mtext = "Jan"
+    elif(month == 2): mtext = "Feb"
+    elif(month == 3): mtext = "Mar"
+    elif(month == 4): mtext = "Apr"
+    elif(month == 5): mtext = "May"
+    elif(month == 6): mtext = "Jun"
+    elif(month == 7): mtext = "Jul"
+    elif(month == 8): mtext = "Aug"
+    elif(month == 9): mtext = "Sep"
+    elif(month == 10): mtext = "Oct"
+    elif(month == 11): mtext = "Nov"
+    elif(month == 12): mtext = "Dec"
+    else: mtext = "NA"
+
+    #GMT + 5:30
+    new_time_local = datetime(year, month, day, hour)
+    return f"{day:02d} {mtext} {str(year)[2:]} {new_time_local.hour:02d}{new_time_local.minute:02d}"
+
+def parseday(d):
+    d = pd.Timestamp(d)
+    day, month, year, hour = d.day, d.month, d.year, d.hour
+    mtext = "Jan"
+    if(month == 1): mtext = "Jan"
+    elif(month == 2): mtext = "Feb"
+    elif(month == 3): mtext = "Mar"
+    elif(month == 4): mtext = "Apr"
+    elif(month == 5): mtext = "May"
+    elif(month == 6): mtext = "Jun"
+    elif(month == 7): mtext = "Jul"
+    elif(month == 8): mtext = "Aug"
+    elif(month == 9): mtext = "Sep"
+    elif(month == 10): mtext = "Oct"
+    elif(month == 11): mtext = "Nov"
+    elif(month == 12): mtext = "Dec"
+    else: mtext = "NA"
+
+    #GMT + 5:30
+    new_time_local = datetime(year, month, day, hour) + timedelta(hours = 5, minutes = 30)
+    return f"{day:02d} {mtext} {str(year)[2:]}"
+
+def makecwr(df):
+    finaldf = pd.DataFrame()
+    finaldf['Time'] = [extractdateforcwr(x) for x in df['Date_IST'].values]
+    finaldf['DDD'] = [f"{int((x//10)*10):03d}" for x in df['wind_direction_10m'].values]
+    finaldf['ff'] = [f"{int(x):02d}" for x in df['wind_speed_10m'].values]
+    finaldf['Wx'] = [wxcode_to_text(x) for x in df['weather_code'].values]
+    finaldf['DB'] = [round(float(x),2) for x in df['temperature_2m'].values]
+    finaldf['DP'] = [round(x,2) for x in df['dew_point_2m'].values]
+    finaldf['RH'] = [int(x) for x in df['relative_humidity_2m'].values]
+    finaldf['Cloud Total'] = [int(x/12.5) for x in df['cloud_cover'].values]
+    finaldf['QNH'] = [int(p) for p in df['surface_pressure'].values]
+    return finaldf
+
+def myround(x, base=5):
+    x = int(x)
+    return base * round(x/base)
+
+def interp_for_levels(ht, vals, new_ht, is_wind_dir = False, round_to=5):
+    if(is_wind_dir):
+        wrapped_winds = np.unwrap(vals, period=360)
+        f = interpolate.interp1d(ht, wrapped_winds ,kind="slinear")
+        interpolated_vals = f(new_ht)
+        actual_vals = []
+        rounded_vals = []
+        for w in interpolated_vals:
+            if(w<0):
+                actual_vals.append(w+360)
+            else:
+                actual_vals.append(w)
+        for v in actual_vals:
+            x = int(v)
+            rounded_vals.append(10 * round(x/10))
+        return rounded_vals
+    else:
+        f = interpolate.interp1d(ht, vals ,kind="slinear")
+        interpolated_vals = f(new_ht)
+        rounded_vals = []
+        for v in interpolated_vals:
+            x = int(v)
+            rounded_vals.append(round_to * round(x/round_to))
+        return rounded_vals